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. The FBMN method brings quantitative analyses, isomeric resolution, including from ion-mobility spectrometry, into molecular networks.

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.

1Molecular networking has become a key method used to visualize and annotate the chemical space in 2 non-targeted mass spectrometry-based experiments. However, distinguishing isomeric compounds and

Herein, we present a protocol for the use of Global Natural Products Social (GNPS) Molecular Networking, an interactive online chemistry-focused mass spectrometry data curation and analysis infrastructure. The goal of GNPS is to provide as much chemical insight for an untargeted tandem mass spectrometry data set as possible and to connect this chemical insight to the underlying biological questions a user wishers to address. This can be performed within one experiment or at the repository scale. GNPS not only serves as a public data repository for untargeted tandem mass spectrometry data with the sample information (metadata), it also captures community knowledge that is disseminated via living data across all public data. One or the main analysis tools used by the GNPS community is molecular networking. Molecular networking creates a structured data table that reflects the chemical space from tandem mass spectrometry experiments via computing the relationships of the tandem mass spectra through spectral similarity. This protocol provides step-by-step instructions for creating reproducible high-quality molecular networks. For training purposes, the reader is led through the protocol from recalling a public data set and its sample information 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.

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

Coral bleaching, a process where corals expel their photosynthetic symbionts, has a profound impact on the health and function of coral reefs. As global ocean temperatures continue to rise, bleaching poses the greatest threat to coral reef ecosystems. Here, untargeted metabolomics was used to analyze the biochemicals in pairs of adjacent corals from a patch reef in Kāneʻohe Bay, Hawaiʻi, where one colony in the pair bleached (in 2015) and recovered while the other did not bleach. There was a strong metabolomic signature of prior bleaching history four years after recovery found in both the host and its algal symbionts. Machine learning analysis determined that the strongest metabolite drivers of the difference in bleaching phenotype were a group of betaine lipids. Those with saturated fatty acids were significantly enriched in thermally tolerant corals and those with longer, unsaturated and diacyl forms were enriched in historically bleached corals. Host immune response molecules, Lyso-PAF and PAF, were also altered by bleaching history and were strongly correlated with symbiont community and algal-derived metabolites suggesting a role of coral immune modulation in symbiont choice and bleaching response. To validate these findings, we tested a separate in situ set of corals and were able to predict the bleaching phenotype with 100% accuracy. Furthermore, corals subjected to an experimental temperature stress had strong phenotype-specific responses in all components of the holobiont, which served to further increase the differences between historical bleaching phenotypes. Thus, we show that natural bleaching susceptibility is simultaneously manifested in the biochemistry of the coral animal and the algal symbiont and that this bleaching history results in different physiological responses to temperature stress. This work provides insight into the biochemical mechanisms involved in coral bleaching and presents a valuable new tool for resilience-based reef restoration.

No abstract

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.