The potential of the diverse chemistries present in natural products (NP) for biotechnology and medicine remains untapped because NP databases are not searchable with raw data and the NP community has no way to share data other than in published papers. Although mass spectrometry techniques are well-suited to high-throughput characterization of natural products, there is a pressing need for an infrastructure to enable sharing and curation of data. We present Global Natural Products Social molecular networking (GNPS, http://gnps.ucsd.edu), an open-access knowledge base for community wide organization and sharing of raw, processed or identified tandem mass (MS/MS) spectrometry data. In GNPS crowdsourced curation of freely available community-wide reference MS libraries will underpin improved annotations. Data-driven social-networking should facilitate identification of spectra and foster collaborations. We also introduce the concept of ‘living data’ through continuous reanalysis of deposited data.

Dereplication represents a key step for rapidly identifying known secondary metabolites in complex biological matrices. In this context, liquid-chromatography coupled to high resolution mass spectrometry (LC-HRMS) is increasingly used and, via untargeted data-dependent MS/MS experiments, massive amounts of detailed information on the chemical composition of crude extracts can be generated. An efficient exploitation of such data sets requires automated data treatment and access to dedicated fragmentation databases. Various novel bioinformatics approaches such as molecular networking (MN) and in-silico fragmentation tools have emerged recently and provide new perspective for early metabolite identification in natural products (NPs) research. Here we propose an innovative dereplication strategy based on the combination of MN with an extensive in-silico MS/MS fragmentation database of NPs. Using two case studies, we demonstrate that this combined approach offers a powerful tool to navigate through the chemistry of complex NPs extracts, dereplicate metabolites, and annotate analogues of database entries.

Untargeted toxicological screening is an analytical challenge, given the high number of molecules and metabolites to be detected and the constant appearance of new psychoactive substances (NPS). The combination of liquid chromatography with high-resolution tandem mass spectrometry (HRMS/MS) in a data-dependent acquisition mode generates a large volume of high quality spectral data. Commercial software for processing MS data acquired during untargeted screening experiments usually compare measured features (mass, retention time, and fragmentation spectra) against a predefined list of analytes. However, there is a lack of tools for visualizing and organizing MS data of unknown compounds. Here, we applied molecular networking to untargeted toxicological screening. This bioinformatic tool allows the exploration and organization of MS/MS data without prior knowledge of the sample's chemical composition. The organization of spectral data is based on spectral similarity.Hence, important information can be obtained even before the annotation step. The link established between molecules enables the propagation of structural information.We applied this approach to three clinical and forensic cases with various matrices: (a) blood and a syringe content in a forensic case of death by self-injection, (b) hair segments in a case of drug-facilitated assault, and (c) urine and blood samples in a case of 3-methoxyphencyclidine intoxication. Data preprocessing with MZmine allows sample-to-sample comparison and generation of multisample molecular networks.Our present study shows that molecular networking can be a useful complement to conventional approaches for untargeted screening interpretation, for example for xenobiotics identification or NPS metabolism elucidation.

Fungal co-cultivation has emerged as a promising way for activating cryptic biosynthetic pathways and discovering novel antimicrobial metabolites. For the success of such studies, a key element remains the development of standardized co-cultivation methods compatible with high-throughput analytical procedures. To efficiently highlight induction processes, it is crucial to acquire a holistic view of intermicrobial communication at the molecular level. To tackle this issue, a strategy was developed based on the miniaturization of fungal cultures that allows for a concomitant survey of induction phenomena in volatile and non-volatile metabolomes. Fungi were directly grown in vials, and each sample was profiled by head space solid phase microextraction gas chromatography mass spectrometry (HS-SPME-GC-MS), while the corresponding solid culture medium was analyzed by liquid chromatography high resolution mass spectrometry (LC-HRMS) after solvent extraction. This strategy was implemented for the screening of volatile and non-volatile metabolite inductions in an ecologically relevant fungal co-culture of Eutypa lata (Pers.) Tul. & C. Tul. (Diatrypaceae) and Botryosphaeria obtusa (Schwein.) Shoemaker (Botryosphaeriaceae), two wooddecaying fungi interacting in the context of esca disease of grapevine. For a comprehensive evaluation of the results, a multivariate data analysis combining Analysis of Variance and Partial Least Squares approaches, namely AMOPLS, was used to explore the complex LC-HRMS and GC-MS datasets and highlight dynamically induced compounds. A time-series study was carried out over 9 days, showing characteristic metabolite induction patterns in both volatile and non-volatile dimensions. Relevant links between the dynamics of expression of specific metabolite production were observed. In addition, the antifungal activity of 2-nonanone, a metabolite incrementally produced over time in the volatile fraction, was assessed against Eutypa lata and Botryosphaeria obtusa in an adapted bioassay set for volatile compounds. This compound has shown antifungal activity on both fungi and was found to be co-expressed with a known

A dichloromethane extract of the roots from the Panamanian plant Swartzia simplex exhibited a strong antifungal activity in a bioautography assay against a genetically modified hypersusceptible strain of Candida albicans. At-line HPLC activity based profiling of the crude extract enabled a precise localization of the antifungal compounds, and dereplication by UHPLC-HRESIMS indicated the presence of potentially new metabolites. Transposition of the HPLC reversed-phase analytical conditions to medium-pressure liquid chromatography (MPLC) allowed an efficient isolation of the major constituents. Minor compounds of interest were isolated from the MPLC fractions using semipreparative HPLC. Using this strategy, 14 diterpenes (1-14) were isolated, with seven (5-10, 14) being new antifungal natural products. The new structures were elucidated using NMR spectroscopy and HRESIMS analysis. The absolute configurations of some of the compounds were elucidated by electronic circular dichroism spectroscopy. The antifungal properties of these compounds were evaluated as their minimum inhibitory concentrations in a dilution assay against both hypersusceptible and wild-type strains of C. albicans and by assessment of their antibiofilm activities. The potential cytological effects on the ultrastructure of C. albicans of the antifungal compounds isolated were evaluated on thin sections by transmission electron microscopy.

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—MS2 BasEd SaMple VectOrization—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.

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