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Untargeted mass spectrometry (MS) experiments produce complex, multidimensional data that are practically impossible to investigate manually. For this reason, computational pipelines are needed to extract relevant information from raw spectral data and convert it into a more comprehensible format. Depending on the sample type and/or goal of the study, a variety of MS platforms can be used for such analysis. MZmine is an open-source software for the processing of raw spectral data generated by different MS platforms. Examples include liquid chromatography-MS, gas chromatography-MS and MS-imaging. These data might typically be associated with various applications including metabolomics and lipidomics. Moreover, the third version of the software, described herein, supports the processing of ion mobility spectrometry (IMS) data. The present protocol provides three distinct procedures to perform feature detection and annotation of untargeted MS data produced by different instrumental setups: liquid chromatography-(IMS-)MS, gas chromatography-MS and (IMS-)MS imaging. For training purposes, example datasets are provided together with configuration batch files (i.e., list of processing steps and parameters) to allow new users to easily replicate the described workflows. Depending on the number of data files and available computing resources, we anticipate this to take between 2 and 24 h for new MZmine users and nonexperts. Within each procedure, we provide a detailed description for all processing parameters together with instructions/recommendations for their optimization. The main generated outputs are represented by aligned feature tables and fragmentation spectra lists that can be used by other third-party tools for further downstream analysis., (© 2024. Springer Nature Limited.)

Natural products exhibit interesting structural features and significant biological activities. The discovery of new bioactive molecules is a complex process that requires high-quality metabolite profiling data to properly target the isolation of compounds of interest and enable their complete structural characterization. The same metabolite profiling data can also be used to better understand chemotaxonomic links between species. This Data Descriptor details a dataset resulting from the untargeted liquid chromatography-mass spectrometry metabolite profiling of 76 natural extracts of the Celastraceae family. The spectral annotation results and related chemical and taxonomic metadata are shared, along with proposed examples of data reuse. This data can be further studied by researchers exploring the chemical diversity of natural products. This can serve as a reference sample set for deep metabolome investigation of this chemically rich plant family., (© 2024. The Author(s).)

Despite the increasing availability of tandem mass spectrometry (MS/MS) community spectral libraries for untargeted metabolomics over the past decade, the majority of acquired MS/MS spectra remain uninterpreted. To further aid in interpreting unannotated spectra, we created a nearest neighbor suspect spectral library, consisting of 87,916 annotated MS/MS spectra derived from hundreds of millions of MS/MS spectra originating from published untargeted metabolomics experiments. Entries in this library, or "suspects," were derived from unannotated spectra that could be linked in a molecular network to an annotated spectrum. Annotations were propagated to unknowns based on structural relationships to reference molecules using MS/MS-based spectrum alignment. We demonstrate the broad relevance of the nearest neighbor suspect spectral library through representative examples of propagation-based annotation of acylcarnitines, bacterial and plant natural products, and drug metabolism. Our results also highlight how the library can help to better understand an Alzheimer's brain phenotype. The nearest neighbor suspect spectral library is openly available for download or for data analysis through the GNPS platform to help investigators hypothesize candidate structures for unknown MS/MS spectra in untargeted metabolomics data., (© 2023. The Author(s).)

The identification of molecular structure is essential for understanding chemical diversity and for developing drug leads from small molecules. Nevertheless, the structure elucidation of small molecules by Nuclear Magnetic Resonance (NMR) experiments is often a long and non-trivial process that relies on years of training. To achieve this process efficiently, several spectral databases have been established to retrieve reference NMR spectra. However, the number of reference NMR spectra available is limited and has mostly facilitated annotation of commercially available derivatives. Here, we introduce DeepSAT, a neural network-based structure annotation and scaffold prediction system that directly extracts the chemical features associated with molecular structures from their NMR spectra. Using only the 1 H- 13 C HSQC spectrum, DeepSAT identifies related known compounds and thus efficiently assists in the identification of molecular structures. DeepSAT is expected to accelerate chemical and biomedical research by accelerating the identification of molecular structures., (© 2023. Springer Nature Switzerland AG.)

Despite advances in sequencing, lack of standardization makes comparisons across studies challenging and hampers insights into the structure and function of microbial communities across multiple habitats on a planetary scale. Here we present a multi-omics analysis of a diverse set of 880 microbial community samples collected for the Earth Microbiome Project. We include amplicon (16S, 18S, ITS) and shotgun metagenomic sequence data, and untargeted metabolomics data (liquid chromatography-tandem mass spectrometry and gas chromatography mass spectrometry). We used standardized protocols and analytical methods to characterize microbial communities, focusing on relationships and co-occurrences of microbially related metabolites and microbial taxa across environments, thus allowing us to explore diversity at extraordinary scale. In addition to a reference database for metagenomic and metabolomic data, we provide a framework for incorporating additional studies, enabling the expansion of existing knowledge in the form of an evolving community resource. We demonstrate the utility of this database by testing the hypothesis that every microbe and metabolite is everywhere but the environment selects. Our results show that metabolite diversity exhibits turnover and nestedness related to both microbial communities and the environment, whereas the relative abundances of microbially related metabolites vary and co-occur with specific microbial consortia in a habitat-specific manner. We additionally show the power of certain chemistry, in particular terpenoids, in distinguishing Earth's environments (for example, terrestrial plant surfaces and soils, freshwater and marine animal stool), as well as that of certain microbes including Conexibacter woesei (terrestrial soils), Haloquadratum walsbyi (marine deposits) and Pantoea dispersa (terrestrial plant detritus). This Resource provides insight into the taxa and metabolites within microbial communities from diverse habitats across Earth, informing both microbial and chemical ecology, and provides a foundation and methods for multi-omics microbiome studies of hosts and the environment., (© 2022. The Author(s).)

Metabolomics is playing an increasingly prominent role in chemical ecology and in the discovery of bioactive natural products (NPs). The identification of metabolites is a common/central objective in both research fields. NPs have significant biological properties and play roles in multiple chemical-ecological interactions. Classically, in pharmacognosy, their chemical structure is determined after a complex process of isolating and interpreting spectroscopic data. With the advent of powerful analytical techniques such as liquid chromatography-mass spectrometry (LC-MS) the annotation process of the specialised metabolome of plants and microorganisms has improved considerably. In this article, we summarise the possibilities opened by these advances and illustrate how we harnessed them in our own research to automate annotations of NPs and target the isolation of key compounds. In addition, we are also discussing the analytical and computational challenges associated with these emerging approaches and their perspective., (Copyright 2022 Jean-Luc Wolfender, Arnaud Gaudry, Adriano Rutz, Luis-Manuel Quiros-Guerrero, Louis-Félix Nothias, Emerson Ferreira Queiroz, Emmanuel Defossez, Pierre-Marie Allard. License: This work is licensed under a Creative Commons Attribution 4.0 International License.)

Collections of natural extracts hold potential for the discovery of novel natural products with original modes of action. The prioritization of extracts from collections remains challenging due to the lack of a workflow that combines multiple-source information to facilitate the data interpretation. Results from different analytical techniques and literature reports need to be organized, processed, and interpreted to enable optimal decision-making for extracts prioritization. Here, we introduce Inventa , a computational tool that highlights the structural novelty potential within extracts, considering untargeted mass spectrometry data, spectral annotation, and literature reports. Based on this information, Inventa calculates multiple scores that inform their structural potential. Thus, Inventa has the potential to accelerate new natural products discovery. Inventa was applied to a set of plants from the Celastraceae family as a proof of concept. The Pristimera indica (Willd.) A.C.Sm roots extract was highlighted as a promising source of potentially novel compounds. Its phytochemical investigation resulted in the isolation and de novo characterization of thirteen new dihydro- β -agarofuran sesquiterpenes, five of them presenting a new 9-oxodihydro- β -agarofuran base scaffold., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Quiros-Guerrero, Nothias, Gaudry, Marcourt, Allard, Rutz, David, Queiroz and Wolfender.)

The exchange of metabolites mediates algal and bacterial interactions that maintain ecosystem function. Yet, while thousands of metabolites are produced, only a few molecules have been identified in these associations. Using the ubiquitous microalgae Pseudo-nitzschia sp., as a model, we employed an untargeted metabolomics strategy to assign structural characteristics to the metabolites that distinguished specific diatom-microbiome associations. We cultured five species of Pseudo-nitzschia, including two species that produced the toxin domoic acid, and examined their microbiomes and metabolomes. A total of 4826 molecular features were detected by tandem mass spectrometry. Only 229 of these could be annotated using available mass spectral libraries, but by applying new in silico annotation tools, characterization was expanded to 2710 features. The metabolomes of the Pseudo-nitzschia-microbiome associations were distinct and distinguished by structurally diverse nitrogen compounds, ranging from simple amines and amides to cyclic compounds such as imidazoles, pyrrolidines and lactams. By illuminating the dark metabolomes, this study expands our capacity to discover new chemical targets that facilitate microbial partnerships and uncovers the chemical diversity that underpins algae-bacteria interactions., (© 2022 Applied Microbiology International and John Wiley & Sons Ltd.)

In natural products research, chemodiverse extracts coming from multiple organisms are explored for novel bioactive molecules, sometimes over extended periods. Samples are usually analyzed by liquid chromatography coupled with fragmentation mass spectrometry to acquire informative mass spectral ensembles. Such data is then exploited to establish relationships among analytes or samples (e.g., via molecular networking) and annotate metabolites. However, the comparison of samples profiled in different batches is challenging with current metabolomics methods since the experimental variation-changes in chromatographical or mass spectrometric conditions - hinders the direct comparison of the profiled samples. Here we introduce MEMO- M S2 Bas E d Sa M ple Vect O rization-a method allowing to cluster large amounts of chemodiverse samples based on their LC-MS/MS profiles in a retention time agnostic manner. This method is particularly suited for heterogeneous and chemodiverse sample sets. MEMO demonstrated similar clustering performance as state-of-the-art metrics considering fragmentation spectra. More importantly, such performance was achieved without the requirement of a prior feature alignment step and in a significantly shorter computational time. MEMO thus allows the comparison of vast ensembles of samples, even when analyzed over long periods of time, and on different chromatographic or mass spectrometry platforms. This new addition to the computational metabolomics toolbox should drastically expand the scope of large-scale comparative analysis., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Gaudry, Huber, Nothias, Cretton, Kaiser, Wolfender and Allard.)

Untargeted metabolomics experiments rely on spectral libraries for structure annotation, but, typically, only a small fraction of spectra can be matched. Previous in silico methods search in structure databases but cannot distinguish between correct and incorrect annotations. Here we introduce the COSMIC workflow that combines in silico structure database generation and annotation with a confidence score consisting of kernel density P value estimation and a support vector machine with enforced directionality of features. On diverse datasets, COSMIC annotates a substantial number of hits at low false discovery rates and outperforms spectral library search. To demonstrate that COSMIC can annotate structures never reported before, we annotated 12 natural bile acids. The annotation of nine structures was confirmed by manual evaluation and two structures using synthetic standards. In human samples, we annotated and manually validated 315 molecular structures currently absent from the Human Metabolome Database. Application of COSMIC to data from 17,400 metabolomics experiments led to 1,715 high-confidence structural annotations that were absent from spectral libraries., (© 2022. The Author(s).)

Metabolites exuded by primary producers comprise a significant fraction of marine dissolved organic matter, a poorly characterized, heterogenous mixture that dictates microbial metabolism and biogeochemical cycling. We present a foundational untargeted molecular analysis of exudates released by coral reef primary producers using liquid chromatography-tandem mass spectrometry to examine compounds produced by two coral species and three types of algae (macroalgae, turfing microalgae, and crustose coralline algae [CCA]) from Mo'orea, French Polynesia. Of 10,568 distinct ion features recovered from reef and mesocosm waters, 1,667 were exuded by producers; the majority (86%) were organism specific, reflecting a clear divide between coral and algal exometabolomes. These data allowed us to examine two tenets of coral reef ecology at the molecular level. First, stoichiometric analyses show a significantly reduced nominal carbon oxidation state of algal exometabolites than coral exometabolites, illustrating one ecological mechanism by which algal phase shifts engender fundamental changes in the biogeochemistry of reef biomes. Second, coral and algal exometabolomes were differentially enriched in organic macronutrients, revealing a mechanism for reef nutrient-recycling. Coral exometabolomes were enriched in diverse sources of nitrogen and phosphorus, including tyrosine derivatives, oleoyl-taurines, and acyl carnitines. Exometabolites of CCA and turf algae were significantly enriched in nitrogen with distinct signals from polyketide macrolactams and alkaloids, respectively. Macroalgal exometabolomes were dominated by nonnitrogenous compounds, including diverse prenol lipids and steroids. This study provides molecular-level insights into biogeochemical cycling on coral reefs and illustrates how changing benthic cover on reefs influences reef water chemistry with implications for microbial metabolism., Competing Interests: The authors declare no competing interest., (Copyright © 2022 the Author(s). Published by PNAS.)

Background: Sponges are ancient sessile metazoans, which form with their associated microbial symbionts a complex functional unit called a holobiont. Sponges are a rich source of chemical diversity; however, there is limited knowledge of which holobiont members produce certain metabolites and how they may contribute to chemical interactions. To address this issue, we applied non-targeted liquid chromatography tandem mass spectrometry (LC-MS/MS) and gas chromatography mass spectrometry (GC-MS) to either whole sponge tissue or fractionated microbial cells from six different, co-occurring sponge species., Results: Several metabolites were commonly found or enriched in whole sponge tissue, supporting the notion that sponge cells produce them. These include 2-methylbutyryl-carnitine, hexanoyl-carnitine and various carbohydrates, which may be potential food sources for microorganisms, as well as the antagonistic compounds hymenialdisine and eicosatrienoic acid methyl ester. Metabolites that were mostly observed or enriched in microbial cells include the antioxidant didodecyl 3,3'-thiodipropionate, the antagonistic compounds docosatetraenoic acid, and immune-suppressor phenylethylamide. This suggests that these compounds are mainly produced by the microbial members in the sponge holobiont, and are potentially either involved in inter-microbial competitions or in defenses against intruding organisms., Conclusions: This study shows how different chemical functionality is compartmentalized between sponge hosts and their microbial symbionts and provides new insights into how chemical interactions underpin the function of sponge holobionts. Video abstract., (© 2022. The Author(s).)

Molecular networking (MN) has become a popular data analysis method for untargeted mass spectrometry (MS)/MS-based metabolomics. Recently, MN has been suggested as a powerful tool for drug metabolite identification, but its effectiveness for drug metabolism studies has not yet been benchmarked against existing strategies. In this study, we compared the performance of MN, mass defect filtering, Agilent MassHunter Metabolite ID, and Agilent Mass Profiler Professional workflows to annotate metabolites of sildenafil generated in an in vitro liver microsome-based metabolism study. Totally, 28 previously known metabolites with 15 additional unknown isomers and 25 unknown metabolites were found in this study. The comparison demonstrated that MN exhibited performances comparable or superior to those of the existing tools in terms of the number of detected metabolites (27 known metabolites and 22 unknown metabolites), ratio of false positives, and the amount of time and effort required for human labor-based postprocessing, which provided evidence of the efficiency of MN as a drug metabolite identification tool.

Computational approaches such as genome and metabolome mining are becoming essential to natural products (NPs) research. Consequently, a need exists for an automated structure-type classification system to handle the massive amounts of data appearing for NP structures. An ideal semantic ontology for the classification of NPs should go beyond the simple presence/absence of chemical substructures, but also include the taxonomy of the producing organism, the nature of the biosynthetic pathway, and/or their biological properties. Thus, a holistic and automatic NP classification framework could have considerable value to comprehensively navigate the relatedness of NPs, and especially so when analyzing large numbers of NPs. Here, we introduce NPClassifier, a deep-learning tool for the automated structural classification of NPs from their counted Morgan fingerprints. NPClassifier is expected to accelerate and enhance NP discovery by linking NP structures to their underlying properties.

Microbial natural products are a major source of bioactive compounds for drug discovery. Among these molecules, nonribosomal peptides (NRPs) represent a diverse class of natural products that include antibiotics, immunosuppressants, and anticancer agents. Recent breakthroughs in natural product discovery have revealed the chemical structure of several thousand NRPs. However, biosynthetic gene clusters (BGCs) encoding them are known only for a few hundred compounds. Here, we developed Nerpa, a computational method for the high-throughput discovery of novel BGCs responsible for producing known NRPs. After searching 13,399 representative bacterial genomes from the RefSeq repository against 8368 known NRPs, Nerpa linked 117 BGCs to their products. We further experimentally validated the predicted BGC of ngercheumicin from Photobacterium galatheae via mass spectrometry. Nerpa supports searching new genomes against thousands of known NRP structures, and novel molecular structures against tens of thousands of bacterial genomes. The availability of these tools can enhance our understanding of NRP synthesis and the function of their biosynthetic enzymes.

Many ant species grow fungus gardens that predigest food as an essential step of the ants' nutrient uptake. These symbiotic fungus gardens have long been studied and feature a gradient of increasing substrate degradation from top to bottom. To further facilitate the study of fungus gardens and enable the understanding of the predigestion process in more detail than currently known, we applied recent mass spectrometry-based approaches and generated a three-dimensional (3D) molecular map of an Atta texana fungus garden to reveal chemical modifications as plant substrates pass through it. The metabolomics approach presented in this study can be applied to study similar processes in natural environments to compare with lab-maintained ecosystems. IMPORTANCE The study of complex ecosystems requires an understanding of the chemical processes involving molecules from several sources. Some of the molecules present in fungus-growing ants' symbiotic system originate from plants. To facilitate the study of fungus gardens from a chemical perspective, we provide a molecular map of an Atta texana fungus garden to reveal chemical modifications as plant substrates pass through it. The metabolomics approach presented in this study can be applied to study similar processes in natural environments.

Molecular networking connects mass spectra of molecules based on the similarity of their fragmentation patterns. However, during ionization, molecules commonly form multiple ion species with different fragmentation behavior. As a result, the fragmentation spectra of these ion species often remain unconnected in tandem mass spectrometry-based molecular networks, leading to redundant and disconnected sub-networks of the same compound classes. To overcome this bottleneck, we develop Ion Identity Molecular Networking (IIMN) that integrates chromatographic peak shape correlation analysis into molecular networks to connect and collapse different ion species of the same molecule. The new feature relationships improve network connectivity for structurally related molecules, can be used to reveal unknown ion-ligand complexes, enhance annotation within molecular networks, and facilitate the expansion of spectral reference libraries. IIMN is integrated into various open source feature finding tools and the GNPS environment. Moreover, IIMN-based spectral libraries with a broad coverage of ion species are publicly available.

Streptomyces scabiei is a key causative agent of common scab disease, which causes significant economic losses to potato growers worldwide. This organism produces several phytotoxins that are known or suspected to contribute to host-pathogen interactions and disease development; however, the full metabolic potential of S. scabiei has not been previously investigated. In this study, we used a combined metabolomic and genomic approach to investigate the metabolites that are produced by S. scabiei . The genome sequence was analyzed using antiSMASH and DeepBGC to identify specialized metabolite biosynthetic gene clusters. Using untargeted liquid chromatography-coupled tandem mass spectrometry (LC-MS 2 ), the metabolic profile of S. scabiei was compared after cultivation on three different growth media. MS 2 data were analyzed using Feature-Based Molecular Networking and hierarchical clustering in BioDendro. Metabolites were annotated by performing a Global Natural Products Social Molecular Networking (GNPS) spectral library search or using Network Annotation Propagation, SIRIUS, MetWork, or Competitive Fragmentation Modeling for Metabolite Identification. Using this approach, we were able to putatively identify new analogues of known metabolites as well as molecules that were not previously known to be produced by S. scabiei . To our knowledge, this study represents the first global analysis of specialized metabolites that are produced by this important plant pathogen., Competing Interests: The authors declare no competing financial interest., (© 2021 The Authors. Published by American Chemical Society.)

Bacterial specialized metabolites are of immense importance because of their medicinal, industrial, and agricultural applications. Streptomyces clavuligerus is a known producer of such compounds; however, much of its metabolic potential remains unknown, as many associated biosynthetic gene clusters are silent or expressed at low levels. The overexpression of ribosome recycling factor ( frr ) and ribosome engineering (induced rpsL mutations) in other Streptomyces spp. has been reported to increase the production of known specialized metabolites. Therefore, we used an overexpression strategy in combination with untargeted metabolomics, molecular networking, and in silico analysis to annotate 28 metabolites in the current study, which have not been reported previously in S. clavuligerus . Many of the newly described metabolites are commonly found in plants, further alluding to the ability of S. clavuligerus to produce such compounds under specific conditions. In addition, the manipulation of frr and rpsL led to different metabolite production profiles in most cases. Known and putative gene clusters associated with the production of the observed compounds are also discussed. This work suggests that the combination of traditional strain engineering and recently developed metabolomics technologies together can provide rapid and cost-effective strategies to further speed up the discovery of novel natural products.

Metabolomics using nontargeted tandem mass spectrometry can detect thousands of molecules in a biological sample. However, structural molecule annotation is limited to structures present in libraries or databases, restricting analysis and interpretation of experimental data. Here we describe CANOPUS (class assignment and ontology prediction using mass spectrometry), a computational tool for systematic compound class annotation. CANOPUS uses a deep neural network to predict 2,497 compound classes from fragmentation spectra, including all biologically relevant classes. CANOPUS explicitly targets compounds for which neither spectral nor structural reference data are available and predicts classes lacking tandem mass spectrometry training data. In evaluation using reference data, CANOPUS reached very high prediction performance (average accuracy of 99.7% in cross-validation) and outperformed four baseline methods. We demonstrate the broad utility of CANOPUS by investigating the effect of microbial colonization in the mouse digestive system, through analysis of the chemodiversity of different Euphorbia plants and regarding the discovery of a marine natural product, revealing biological insights at the compound class level.

Untargeted mass spectrometry is employed to detect small molecules in complex biospecimens, generating data that are difficult to interpret. We developed Qemistree, a data exploration strategy based on the hierarchical organization of molecular fingerprints predicted from fragmentation spectra. Qemistree allows mass spectrometry data to be represented in the context of sample metadata and chemical ontologies. By expressing molecular relationships as a tree, we can apply ecological tools that are designed to analyze and visualize the relatedness of DNA sequences to metabolomics data. Here we demonstrate the use of tree-guided data exploration tools to compare metabolomics samples across different experimental conditions such as chromatographic shifts. Additionally, we leverage a tree representation to visualize chemical diversity in a heterogeneous collection of samples. The Qemistree software pipeline is freely available to the microbiome and metabolomics communities in the form of a QIIME2 plugin, and a global natural products social molecular networking workflow.

We engineered a machine learning approach, MSHub, to enable auto-deconvolution of gas chromatography-mass spectrometry (GC-MS) data. We then designed workflows to enable the community to store, process, share, annotate, compare and perform molecular networking of GC-MS data within the Global Natural Product Social (GNPS) Molecular Networking analysis platform. MSHub/GNPS performs auto-deconvolution of compound fragmentation patterns via unsupervised non-negative matrix factorization and quantifies the reproducibility of fragmentation patterns across samples.

Interpretation of fragmentation mass spectra depends on our knowledge of collision-induced dissociation mechanisms. Computational methods for the annotation of fragmentation mechanisms operate within the boundaries of recognized fragmentation pathways. The prevalence of charge migration fragmentation (CMF) in sodiated ion fragmentation spectra, which produces nonsodiated fragment ions, is unknown. Here, we investigated the extent of CMF in the fragmentation spectra of sodiated precursors by mining the NIST17 spectral library using a diagnostic mass difference. Our results showed that a substantial amount of fragment ions in sodiated precursor spectra are derived from CMF, indicating that this fragmentation mechanism should be commonly considered by computational methods for compound annotation.

The Panamanian rocket frog Colostethus panamansis (family Dendrobatidae) has been affected by chytridiomycosis, a deadly disease caused by the fungus Batrachochytrium dendrobatidis ( Bd ). While there are still uninfected frogs, we set out to isolate microbes from anatomically distinct regions in an effort to create a cultivable resource within Panama for potential drug/agricultural/ecological applications that perhaps could also be used as part of a strategy to protect frogs from infections. To understand if there are specific anatomies that should be explored in future applications of this resource, we mapped skin-associated bacteria of C. panamansis and their metabolite production potential by mass spectrometry on a 3D model. Our results indicate that five bacterial families (Enterobacteriaceae, Comamonadaceae, Aeromonadaceae, Staphylococcaceae and Pseudomonadaceae) dominate the cultivable microbes from the skin of C. panamansis . The combination of microbial classification and molecular analysis in relation to the anti- Bd inhibitory databases reveals the resource has future potential for amphibian conservation.

Molecular networking has become a key method to visualize and annotate the chemical space in non-targeted mass spectrometry data. We present feature-based molecular networking (FBMN) as an analysis method in the Global Natural Products Social Molecular Networking (GNPS) infrastructure that builds on chromatographic feature detection and alignment tools. FBMN enables quantitative analysis and resolution of isomers, including from ion mobility spectrometry.

We present ReDU ( https://redu.ucsd.edu/ ), a system for metadata capture of public mass spectrometry-based metabolomics data, with validated controlled vocabularies. Systematic capture of knowledge enables the reanalysis of public data and/or co-analysis of one's own data. ReDU enables multiple types of analyses, including finding chemicals and associated metadata, comparing the shared and different chemicals between groups of samples, and metadata-filtered, repository-scale molecular networking.

Dark teas are prepared by a microbial fermentation process. Flavan-3-ol B-ring fission analogues (FBRFAs) are some of the key bioactive constituents that characterize dark teas. The precursors and the synthetic mechanism involved in the formation of FBRFAs are not known. Using a unique solid-state fermentation system with β-cyclodextrin inclusion complexation as well as targeted chromatographic isolation, spectroscopic identification, and Feature-based Molecular Networking on the Global Natural Products Social Molecular Networking web platform, we reveal that dihydromyricetin and the FBRFAs, including teadenol A and fuzhuanin A, are derived from epigallocatechin gallate upon exposure to fungal strains isolated from Fuzhuan brick tea. In particular, the strains from subphylum Pezizomycotina were key drivers for these B-/C-ring oxidation transformations. These are the same transformations seen during the fermentation process of dark teas. These discoveries set the stage to enrich dark teas and other food products for these health-promoting constituents.

Latent HIV reservoirs are the main obstacle to eradicate HIV infection. One strategy proposes to eliminate these viral reservoirs by pharmacologically reactivating the latently infected T cells. We show here that a 4-deoxyphorbol ester derivative isolated from Euphorbia amygdaloides ssp. semiperfoliata, 4β-dPE A, reactivates HIV-1 from latency and could potentially contribute to decrease the viral reservoir. 4β-dPE A shows two effects in the HIV replication cycle, infection inhibition and HIV transactivation, similarly to other phorboids PKC agonists such PMA and prostratin and to other diterpene esters such SJ23B. Our data suggest 4β-dPE A is non-tumorigenic, unlike the related compound PMA. As the compounds are highly similar, the lack of tumorigenicity by 4β-dPE A could be due to the lack of a long side lipophilic chain that is present in PMA. 4β-dPE activates HIV transcription at nanomolar concentrations, lower than the concentration needed by other latency reversing agents (LRAs) such as prostratin and similar to bryostatin. PKCθ/MEK activation is required for the transcriptional activity, and thus, anti-latency activity of 4β-dPE A. However, CD4, CXCR4 and CCR5 receptors down-regulation effect seems to be independent of PCK/MEK, suggesting the existence of at least two different targets for 4β-dPE A. Furthermore, NF-κb transcription factor is involved in 4β-dPE HIV reactivation, as previously shown for other PKCs agonists. We also studied the effects of 4β-dPE A in combination with other LRAs. When 4β-dPE A was combined with another PKC agonists such as prostratin an antagonic effect was achieved, while, when combined with an HDAC inhibitor such as vorinostat, a strong synergistic effect was obtained. Interestingly, the latency reversing effect of the combination was synergistically diminishing the EC 50 value but also increasing the efficacy showed by the drugs alone. In addition, combinations of 4β-dPE A with antiretroviral drugs as CCR5 antagonist, NRTIs, NNRTIs and PIs, showed a consistent synergistic effect, suggesting that the combination would not interefer with antiretroviral therapy (ART). Finally, 4β-dPE A induced latent HIV reactivation in CD4 + T cells of infected patients under ART at similar levels than the tumorigenic phorbol derivative PMA, showing a clear reactivation effect. In summary, we describe here the mechanism of action of a new potent deoxyphorbol derivative as a latency reversing agent candidate to decrease the size of HIV reservoirs., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Global Natural Product Social Molecular Networking (GNPS) is an interactive online small molecule-focused tandem mass spectrometry (MS 2 ) data curation and analysis infrastructure. It is intended to provide as much chemical insight as possible into an untargeted MS 2 dataset and to connect this chemical insight to the user's underlying biological questions. This can be performed within one liquid chromatography (LC)-MS 2 experiment or at the repository scale. GNPS-MassIVE is a public data repository for untargeted MS 2 data with sample information (metadata) and annotated MS 2 spectra. These publicly accessible data can be annotated and updated with the GNPS infrastructure keeping a continuous record of all changes. This knowledge is disseminated across all public data; it is a living dataset. Molecular networking-one of the main analysis tools used within the GNPS platform-creates a structured data table that reflects the molecular diversity captured in tandem mass spectrometry experiments by computing the relationships of the MS 2 spectra as spectral similarity. This protocol provides step-by-step instructions for creating reproducible, high-quality molecular networks. For training purposes, the reader is led through a 90- to 120-min procedure that starts by recalling an example public dataset and its sample information and proceeds to creating and interpreting a molecular network. Each data analysis job can be shared or cloned to disseminate the knowledge gained, thus propagating information that can lead to the discovery of molecules, metabolic pathways, and ecosystem/community interactions.

Alnus spp. (Betulaceae) have been used for treatments of hemorrhage, burn injuries, antipyretic fever, diarrhea, and alcoholism in traditional medicines. In this study, a digitized LC-MS/MS data analysis workflow was applied to provide an overview on chemical diversity of 15 Alnus extracts prepared from bark, twigs, leaves, and fruits of A. japonica, A. firma, A. hirsuta, and A. hirsuta var. sibirica. Most of the MS/MS spectra could be putatively annotated based on library matching, in silico fragmentation, and substructural topic modeling. The putative annotation allowed us to discriminate the extracts into three chemotypes based on dominant chemical scaffolds: diarylheptanoids, flavonoids or tannins. This high-throughput chemical annotation was correlated with α-glucosidase inhibition data of extracts, and it allowed us to identify gallic acid as the major active compound of A. firma., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

This report describes the first application of the novel NMR-based machine learning tool "Small Molecule Accurate Recognition Technology" (SMART 2.0) for mixture analysis and subsequent accelerated discovery and characterization of new natural products. The concept was applied to the extract of a filamentous marine cyanobacterium known to be a prolific producer of cytotoxic natural products. This environmental Symploca extract was roughly fractionated, and then prioritized and guided by cancer cell cytotoxicity, NMR-based SMART 2.0, and MS 2 -based molecular networking. This led to the isolation and rapid identification of a new chimeric swinholide-like macrolide, symplocolide A, as well as the annotation of swinholide A, samholides A-I, and several new derivatives. The planar structure of symplocolide A was confirmed to be a structural hybrid between swinholide A and luminaolide B by 1D/2D NMR and LC-MS 2 analysis. A second example applies SMART 2.0 to the characterization of structurally novel cyclic peptides, and compares this approach to the recently appearing "atomic sort" method. This study exemplifies the revolutionary potential of combined traditional and deep learning-assisted analytical approaches to overcome longstanding challenges in natural products drug discovery.

Ribosomally synthesized and post-translationally modified peptides (RiPPs) are an important class of natural products that contain antibiotics and a variety of other bioactive compounds. The existing methods for discovery of RiPPs by combining genome mining and computational mass spectrometry are limited to discovering specific classes of RiPPs from small datasets, and these methods fail to handle unknown post-translational modifications. Here, we present MetaMiner, a software tool for addressing these challenges that is compatible with large-scale screening platforms for natural product discovery. After searching millions of spectra in the Global Natural Products Social (GNPS) molecular networking infrastructure against just eight genomic and metagenomic datasets, MetaMiner discovered 31 known and seven unknown RiPPs from diverse microbial communities, including human microbiome and lichen microbiome, and microorganisms isolated from the International Space Station., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Naegleria fowleri is a free-living amoeba causing primary amoebic meningoencephalitis, a rapid-onset brain infection in humans with over 97% mortality rate. Despite some progress in the treatment of the disease, there is no single, proven, evidence-based treatment with a high probability of cure. Here we report the chemical library screening and experimental identification of four new compounds with amoebicidal effects against N. fowleri . The chemical library was screened by molecular docking against a homology model of sterol Δ8-Δ7 isomerase (NfERG2). Thirty top-ranking hits were then tested in a cell-based assay for antiproliferative/amoebicidal activities. Eight chemicals exhibited nearly 100% inhibition of N. fowleri at 50 μM, with the EC 50 values ranging from 6 to 25 μM. A cell toxicity assay using human HEK-293 cells was also performed. Four of the compounds preferentially kill amoeba cells with no apparent human cell toxicities. These compounds fall into two distinct chemical scaffolds with druglike properties.

Integrating multiomics datasets is critical for microbiome research; however, inferring interactions across omics datasets has multiple statistical challenges. We solve this problem by using neural networks (https://github.com/biocore/mmvec) to estimate the conditional probability that each molecule is present given the presence of a specific microorganism. We show with known environmental (desert soil biocrust wetting) and clinical (cystic fibrosis lung) examples, our ability to recover microbe-metabolite relationships, and demonstrate how the method can discover relationships between microbially produced metabolites and inflammatory bowel disease.

Clavulanic acid is a bacterial specialized metabolite, which inhibits certain serine β-lactamases, enzymes that inactivate β-lactam antibiotics to confer resistance. Due to this activity, clavulanic acid is widely used in combination with penicillin and cephalosporin (β-lactam) antibiotics to treat infections caused by β-lactamase-producing bacteria. Clavulanic acid is industrially produced by fermenting Streptomyces clavuligerus , as large-scale chemical synthesis is not commercially feasible. Other than S. clavuligerus , Streptomyces jumonjinensis and Streptomyces katsurahamanus also produce clavulanic acid along with cephamycin C, but information regarding their genome sequences is not available. In addition, the Streptomyces contain many biosynthetic gene clusters thought to be "cryptic," as the specialized metabolites produced by them are not known. Therefore, we sequenced the genomes of S. jumonjinensis and S. katsurahamanus , and examined their metabolomes using untargeted mass spectrometry along with S. clavuligerus for comparison. We analyzed the biosynthetic gene cluster content of the three species to correlate their biosynthetic capacities, by matching them with the specialized metabolites detected in the current study. It was recently reported that S. clavuligerus can produce the plant-associated metabolite naringenin, and we describe more examples of such specialized metabolites in extracts from the three Streptomyces species. Detailed comparisons of the biosynthetic gene clusters involved in clavulanic acid (and cephamycin C) production were also performed, and based on our analyses, we propose the core set of genes responsible for producing this medicinally important metabolite., (Copyright © 2019 AbuSara, Piercey, Moore, Shaikh, Nothias, Srivastava, Cruz-Morales, Dorrestein, Barona-Gómez and Tahlan.)

Metabolomics has started to embrace computational approaches for chemical interpretation of large data sets. Yet, metabolite annotation remains a key challenge. Recently, molecular networking and MS2LDA emerged as molecular mining tools that find molecular families and substructures in mass spectrometry fragmentation data. Moreover, in silico annotation tools obtain and rank candidate molecules for fragmentation spectra. Ideally, all structural information obtained and inferred from these computational tools could be combined to increase the resulting chemical insight one can obtain from a data set. However, integration is currently hampered as each tool has its own output format and efficient matching of data across these tools is lacking. Here, we introduce MolNetEnhancer, a workflow that combines the outputs from molecular networking, MS2LDA, in silico annotation tools (such as Network Annotation Propagation or DEREPLICATOR), and the automated chemical classification through ClassyFire to provide a more comprehensive chemical overview of metabolomics data whilst at the same time illuminating structural details for each fragmentation spectrum. We present examples from four plant and bacterial case studies and show how MolNetEnhancer enables the chemical annotation, visualization, and discovery of the subtle substructural diversity within molecular families. We conclude that MolNetEnhancer is a useful tool that greatly assists the metabolomics researcher in deciphering the metabolome through combination of multiple independent in silico pipelines.

Coevolutionary theory suggests that an arms race between plants and herbivores yields increased plant specialized metabolite diversity and the geographic mosaic theory of coevolution predicts that coevolutionary interactions vary across geographic scales. Consequently, plant specialized metabolite diversity is expected to be highest in coevolutionary hotspots, geographic regions, which exhibit strong reciprocal selection on the interacting species. Despite being well-established theoretical frameworks, technical limitations have precluded rigorous hypothesis testing. Here we aim at understanding how geographic separation over evolutionary time may have impacted chemical differentiation in the cosmopolitan plant genus Euphorbia . We use a combination of state-of-the-art computational mass spectral metabolomics tools together with cell-based high-throughput immunomodulatory testing. Our results show significant differences in specialized metabolite diversity across geographically separated phylogenetic clades. Chemical structural diversity of the highly toxic Euphorbia diterpenoids is significantly reduced in species native to the Americas, compared to Afro-Eurasia. The localization of these compounds to young stems and roots suggest a possible ecological relevance in herbivory defense. This is further supported by reduced immunomodulatory activity in the American subclade as well as herbivore distribution patterns. We conclude that computational mass spectrometric metabolomics coupled with relevant ecological data provide a strong tool for exploring plant specialized metabolite diversity in a chemo-evolutionary framework.

The species Euphorbia pithyusa and Euphorbia cupanii are two closely related Mediterranean spurges for which their taxonomic relationships are still being debated. Herein, the diterpene ester content of E. cupanii was investigated using liquid chromatography coupled to tandem mass spectrometry. The use of molecular networking coupled to unsupervised substructure annotation ( MS2LDA) indicated the presence of new premyrsinane/myrsinane diterpene esters in the E. cupanii fractions. A structure-guided isolation procedure yielded 16 myrsinane (11a-h, 12, and 13) and premyrsinane esters (14a-c and 15a-c), along with four 4β-phorbol esters (16a-c and 17) that showed inhibitory activity against chikungunya virus replication. The structures of the 16 new compounds (11a-c, 11h, 12, 13, 14a-c, 15a-c, 16a-c, and 17) were characterized by NMR spectroscopy and X-ray crystallography. To further uncover the diterpene ester content of these two species, the concept of combinatorial network annotation propagation (C-NAP) was developed. By leveraging the fact that the diterpene esters of Euphorbia species are made up of limited building blocks, a combinatorial database of theoretical structures was created and used for C-NAP that made possible the annotation of 123 premyrsinane or myrsinane esters, from which 74% are not found in any compound database.

Amphibian populations worldwide have declined and in some cases become extinct due to chytridiomycosis, a pandemic disease caused by the fungus Batrachochytrium dendrobatidis; however, some species have survived these fungal epidemics. Previous studies have suggested that the resistance of these species is due to the presence of cutaneous bacteria producing antifungal metabolites. As our understanding of these metabolites is still limited, we assessed the potential of such compounds against human-relevant fungi such as Aspergillus. In this work we isolated 201 bacterial strains from fifteen samples belonging to seven frog species collected in the highlands of Panama and tested them against Aspergillus fumigatus. Among the 29 bacterial isolates that exhibited antifungal activity, Pseudomonas cichorii showed the greatest inhibition. To visualize the distribution of compounds and identify them in the inhibition zone produced by P. cichorii, we employed MALDI imaging mass spectrometry (MALDI IMS) and MS/MS molecular networking. We identified viscosin and massetolides A, F, G and H in the inhibition zone. Furthermore, viscosin was isolated and evaluated in vitro against A. fumigatus and B. dendrobatidis showing MIC values of 62.50 µg/mL and 31.25 µg/mL, respectively. This is the first report of cyclic depsipeptides with antifungal activity isolated from frog cutaneous bacteria.

Natural products have traditionally been rich sources for drug discovery. In order to clear the road toward the discovery of unknown natural products, biologists need dereplication strategies that identify known ones. Here we report DEREPLICATOR+, an algorithm that improves on the previous approaches for identifying peptidic natural products, and extends them for identification of polyketides, terpenes, benzenoids, alkaloids, flavonoids, and other classes of natural products. We show that DEREPLICATOR+ can search all spectra in the recently launched Global Natural Products Social molecular network and identify an order of magnitude more natural products than previous dereplication efforts. We further demonstrate that DEREPLICATOR+ enables cross-validation of genome-mining and peptidogenomics/glycogenomics results.