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.

Integrating the governing chemistry with the genomics and phenotypes of microbial colonies has been a "holy grail" in microbiology. This work describes a highly sensitive, broadly applicable, and costeffective approach that allows metabolic profiling of live microbial colonies directly from a Petri dish without any sample preparation. Nanospray desorption electrospray ionization mass spectrometry (MS), combined with alignment of MS data and molecular networking, enabled monitoring of metabolite production from live microbial colonies from diverse bacterial genera, including Bacillus subtilis, Streptomyces coelicolor, Mycobacterium smegmatis, and Pseudomonas aeruginosa. This work demonstrates that, by using these tools to visualize small molecular changes within bacterial interactions, insights can be gained into bacterial developmental processes as a result of the improved organization of MS/MS data. To validate this experimental platform, metabolic profiling was performed on Pseudomonas sp. SH-C52, which protects sugar beet plants from infections by specific soil-borne fungi [R. Mendes et al. (2011) Science 332:1097-1100]. The antifungal effect of strain SH-C52 was attributed to thanamycin, a predicted lipopeptide encoded by a nonribosomal peptide synthetase gene cluster. Our technology, in combination with our recently developed peptidogenomics strategy, enabled the detection and partial characterization of thanamycin and showed that it is a monochlorinated lipopeptide that belongs to the syringomycin family of antifungal agents. In conclusion, the platform presented here provides a significant advancement in our ability to understand the spatiotemporal dynamics of metabolite production in live microbial colonies and communities. ambient mass spectrometry | microbial ecology | natural products M icrobes use secreted factors to interact, communicate with, and manipulate their local environment and neighboring cell populations in a process known as metabolic exchange (1-5). By using a wide breadth of molecules ranging from signaling compounds to defensive metabolites, metabolic exchange dictates not only basic microbial behavior, such as biofilm formation, sporulation, and motility, but also social interactions, such as syntrophy and quorum sensing, which enables microbes to establish communities (1-5). Despite these secreted factors, also known as the parvome, having a major impact on the phenotypic development of microbial populations, there is a lack of tools that enable scientists to probe the chemistry of microbial colonies in a direct manner. Currently, the chemistry of microbes is usually studied by monitoring individual molecular species and requires a significant time and monetary investment. Our laboratories are interested in the development of tools that make this process more efficient as well as making it easier for nonchemists to study the chemistry of microbes and nonmicrobe cell populations. Ideally, these tools should be easy to implement, compatible with existing infrastructure, and easily inco...

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.

High-mass-resolution imaging mass spectrometry promises to localize hundreds of metabolites in tissues, cell cultures, and agar plates with cellular resolution, but it is hampered by the lack of bioinformatics tools for automated metabolite identification. We report pySM, a framework for false discovery rate (FDR)-controlled metabolite annotation at the level of the molecular sum formula, for high-mass-resolution imaging mass spectrometry (https://github.com/alexandrovteam/pySM). We introduce a metabolite-signal match score and a target-decoy FDR estimate for spatial metabolomics.

Highlights d Human intestinal epithelium cells (hIECs) can be infected by SARS-CoV-2 d hIECs support SARS-CoV-2 replication and produce de novo viruses d SARS-CoV-2 infection can be controlled in hIECs by type I and III interferons

The human skin is an organ with a surface area of 1.5-2 m 2 that provides our interface with the environment. The molecular composition of this organ is derived from host cells, microbiota, and external molecules. The chemical makeup of the skin surface is largely undefined. Here we advance the technologies needed to explore the topographical distribution of skin molecules, using 3D mapping of mass spectrometry data and microbial 16S rRNA amplicon sequences. Our 3D maps reveal that the molecular composition of skin has diverse distributions and that the composition is defined not only by skin cells and microbes but also by our daily routines, including the application of hygiene products. The technological development of these maps lays a foundation for studying the spatial relationships of human skin with hygiene, the microbiota, and environment, with potential for developing predictive models of skin phenotypes tailored to individual health.human skin | 3D mapping | mass spectrometry | 16S rRNA T he skin provides the interface between our internal molecular processes and the external environment. The stratum corneum (SC), the outer layer of the epidermis, is the most exposed organ of the human body and is composed of molecules derived from our own skin cells, our microbiota, and the environment (1). In addition, it may be possible that personal hygiene products, the clothing we wear, and the food we eat can affect the chemical composition of the skin (2, 3). The chemical environment of the skin is also critical for the development of microbial communities, yet there are no systematic workflows that can be used to correlate skin chemistry and microbes. Human skin microbiome inventories are changing our views of commensal organisms and their roles in establishing and maintaining the immune system and general epithelial health (4-7), but their role in biotransformation of skin molecules is as yet poorly characterized.Understanding the molecular topography of the surface of the SC will provide insights into the chemical environment in which microbes reside on the skin and how in turn they modify this environment. To understand the relationship between the chemical milieu and the microbial communities, we must develop workflows that map the chemistry and microbiology of the human skin and that determine the associations between them. The composition of the human skin microbiota has been correlated with skin anatomy, dividing into moist, dry, and sebaceous microenvironments, and can be influenced by beauty and hygiene products (2-9). Regions such as the face, chest, and back, areas with a high density of sebaceous glands, promote growth of lipophilic microorganisms such as Propionibacterium and Malassezia. In contrast, less exposed regions, such as the groin, axilla, and toe web, which are higher in temperature and moisture content, are colonized by Staphylococcus and Corynebacterium (7, 10, 11). Previous studies have reported mass spectrometry (MS) methods to identify compounds from human skin (12, 13). However, th...

Soils host diverse microbial communities that include filamentous actinobacteria (actinomycetes). These bacteria have been a rich source of useful metabolites, including antimicrobials, antifungals, anticancer agents, siderophores, and immunosuppressants. While humans have long exploited these compounds for therapeutic purposes, the role these natural products may play in mediating interactions between actinomycetes has been difficult to ascertain. As an initial step toward understanding these chemical interactions at a systems level, we employed the emerging techniques of nanospray desorption electrospray ionization (NanoDESI) and matrix-assisted laser desorption ionization–time of flight (MALDI-TOF) imaging mass spectrometry to gain a global chemical view of the model bacterium Streptomyces coelicolor interacting with five other actinomycetes. In each interaction, the majority of secreted compounds associated with S. coelicolor colonies were unique, suggesting an idiosyncratic response from S. coelicolor. Spectral networking revealed a family of unknown compounds produced by S. coelicolor during several interactions. These compounds constitute an extended suite of at least 12 different desferrioxamines with acyl side chains of various lengths; their production was triggered by siderophores made by neighboring strains. Taken together, these results illustrate that chemical interactions between actinomycete bacteria exhibit high complexity and specificity and can drive differential secondary metabolite production.

It is a common problem in natural product therapeutic lead discovery programs that despite good bioassay results in the initial extract, the active compound(s) may not be isolated during subsequent bioassay-guided purification. Herein, we present the concept of bioactive molecular networking to find candidate active molecules directly from fractionated bioactive extracts. By employing tandem mass spectrometry, it is possible to accelerate the dereplication of molecules using molecular networking prior to subsequent isolation of the compounds, and it is also possible to expose potentially bioactive molecules using bioactivity score prediction. Indeed, bioactivity score prediction can be calculated with the relative abundance of a molecule in fractions and the bioactivity level of each fraction. For that reason, we have developed a bioinformatic workflow able to map bioactivity score in molecular networks and applied it for discovery of antiviral compounds from a previously investigated extract of Euphorbia dendroides where the bioactive candidate molecules were not discovered following a classical bioassay-guided fractionation procedure. It can be expected that this approach will be implemented as a systematic strategy, not only in current and future bioactive lead discovery from natural extract collections but also for the reinvestigation of the untapped reservoir of bioactive analogues in previous bioassay-guided fractionation efforts.