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.

Pseudomonads are cosmopolitan microbes able to produce a wide array of specialized metabolites. These molecules allow Pseudomonas to scavenge nutrients, sense population density, and enhance or inhibit growth of competing microbes. However, these valuable metabolites are typically characterized one-molecule-one-microbe at a time instead of inventoried in large numbers. To index and map the diversity of molecules detected from these organisms, 260 strains of ecologically diverse origins were subjected to mass spectrometry-based molecular networking. Molecular networking not only enables dereplication of molecules, but also sheds light on their structural relationships. Moreover, it accelerates discovery of new molecules. Herein, through indexing the Pseudomonas specialized metabolome, we report the molecular networking-based discovery of four molecules and their evolutionary relationships: a poaeamide analog, and a molecular sub-family of cyclic lipopeptides, the bananamides 1, 2, and 3. Analysis of their biosynthetic gene cluster shows that it constitutes a distinct evolutionary branch of the Pseudomonas cyclic lipopeptides. Through analysis of an additional 370 extracts of wheat-associated Pseudomonas, we demonstrate how the detailed knowledge from our reference index can be efficiently propagated to annotate complex metabolomic data from other studies akin to the way newly generated genomic information can be compared to data from public databases.

Dental caries, one of the most globally widespread infectious diseases, is intimately linked to pH dynamics. In supragingival plaque, after the addition of a carbohydrate source, bacterial metabolism decreases the pH which then subsequently recovers. Molecular mechanisms supporting this important homeostasis are poorly characterized in part due to the fact that there are hundreds of active species in dental plaque. Only a few mechanisms (for example, lactate fermentation, the arginine deiminase system) have been identified and studied in detail. Here, we conducted what is to our knowledge, the first full transcriptome and metabolome analysis of a diverse oral plaque community by using a functionally and taxonomically robust in vitro model system greater than 100 species. Differential gene expression analyses from the complete transcriptome of 14 key community members revealed highly varied regulation of both known and previously unassociated pH-neutralizing pathways as a response to the pH drop. Unique expression and metabolite signatures from 400 detected metabolites were found for each stage along the pH curve suggesting it may be possible to define healthy and diseased states of activity. Importantly, for the maintenance of healthy plaque pH, gene transcription activity of known and previously unrecognized pH-neutralizing pathways was associated with the genera Lactobacillus, Veillonella and Streptococcus during the pH recovery phase. Our in vitro study provides a baseline for defining healthy and disease-like states and highlights the power of moving beyond single and dual species applications to capture key players and their orchestrated metabolic activities within a complex human oral microbiome model.

Nonribosomally and ribosomally synthesized bioactive peptides constitute a source of molecules of great biomedical importance, including antibiotics such as penicillin, immunosuppressants such as cyclosporine, and cytostatics such as bleomycin. Recently, an innovative mass-spectrometry-based strategy, peptidogenomics, has been pioneered to effectively mine microbial strains for novel peptidic metabolites. Even though mass-spectrometric peptide detection can be performed quite fast, true high-throughput natural product discovery approaches have still been limited by the inability to rapidly match the identified tandem mass spectra to the gene clusters responsible for the biosynthesis of the corresponding compounds. With Pep2Path, we introduce a software package to fully automate the peptidogenomics approach through the rapid Bayesian probabilistic matching of mass spectra to their corresponding biosynthetic gene clusters. Detailed benchmarking of the method shows that the approach is powerful enough to correctly identify gene clusters even in data sets that consist of hundreds of genomes, which also makes it possible to match compounds from unsequenced organisms to closely related biosynthetic gene clusters in other genomes. Applying Pep2Path to a data set of compounds without known biosynthesis routes, we were able to identify candidate gene clusters for the biosynthesis of five important compounds. Notably, one of these clusters was detected in a genome from a different subphylum of Proteobacteria than that in which the molecule had first been identified. All in all, our approach paves the way towards high-throughput discovery of novel peptidic natural products. Pep2Path is freely available from http://pep2path.sourceforge.net/, implemented in Python, licensed under the GNU General Public License v3 and supported on MS Windows, Linux and Mac OS X.