GNPS is an open-access community-curated analysis platform for sharing natural product mass spectrometry data that enables continuous, automatic reanalysis of deposited 'living' data sets. 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 (MS) techniques are well-suited to high-throughput characterization of NP, 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.

Many antibiotics inhibit the growth of sensitive bacteria by interfering with ribosome function. However, discovery of new protein synthesis inhibitors is curbed by the lack of facile techniques capable of readily identifying antibiotic target sites and modes of action. Furthermore, the frequent rediscovery of known antibiotic scaffolds, especially in natural product extracts, is time-consuming and expensive and diverts resources that could be used toward the isolation of novel lead molecules. In order to avoid these pitfalls and improve the process of dereplication of chemically complex extracts, we designed a two-pronged approach for the ch aracterization of i nhibitors of p rotein s ynthesis (ChIPS) that is suitable for the rapid identification of the site and mode of action on the bacterial ribosome. First, we engineered antibiotic-hypersensitive Escherichia coli strains that contain only one rRNA operon. These strains are used for the rapid isolation of resistance mutants in which rRNA mutations identify the site of the antibiotic action. Second, we show that patterns of drug-induced ribosome stalling on mRNA, monitored by primer extension, can be used to elucidate the mode of antibiotic action. These analyses can be performed within a few days and provide a rapid and efficient approach for identifying the site and mode of action of translation inhibitors targeting the bacterial ribosome. Both techniques were validated using a bacterial strain whose culture extract, composed of unknown metabolites, exhibited protein synthesis inhibitory activity; we were able to rapidly detect the presence of the antibiotic chloramphenicol.

Two new (1 and 2) and one known phenazine derivative (lavanducyanin, 3) were isolated and identified from the fermentation broth of a marine-derived Streptomyces sp. (strain CNS284). In mammalian cell culture studies, compounds 1, 2 and 3 inhibited TNF-α-induced NFκB activity (IC₅₀ values of 4.1, 24.2, and 16.3 μM, respectively) and LPS-induced nitric oxide production (IC₅₀ values of >48.6, 15.1, and 8.0 μM, respectively). PGE₂ production was blocked with greater efficacy (IC₅₀ values of 7.5, 0.89, and 0.63 μM, respectively), possibly due to inhibition of cyclooxygenases in addition to the expression of COX-2. Treatment of cultured HL-60 cells led to dose-dependent accumulation in the subG1 compartment of the cell cycle, as a result of apoptosis. These data provide greater insight on the biological potential of phenazine derivatives, and some guidance on how various substituents may alter potential anti-inflammatory and anti-cancer effects.

For decades, researchers have lacked the ability to rapidly correlate microbial identity with bacterial metabolism. Since specialized metabolites are critical to bacterial function and survival in the environment, we designed a data acquisition and bioinformatics technique (IDBac) that utilizes in situ matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) to analyze protein and specialized metabolite spectra recorded from single bacterial colonies picked from agar plates. We demonstrated the power of our approach by discriminating between two Bacillus subtilis strains in 99% sequence similarity in the 16S rRNA gene. Finally, we used IDBac to simultaneously extract protein and specialized metabolite MS profiles from unidentified Lake Michigan sponge-associated bacteria isolated from an agar plate. In just 3 h, we created hierarchical protein MS groupings of 11 environmental isolates (10 MS replicates each, for a total of 110 spectra) that accurately mirrored phylogenetic groupings. We further distinguished isolates within these groupings, which share nearly identical 16S rRNA gene sequence identity, based on interspecies and intraspecies differences in specialized metabolite production. IDBac is an attempt to couple in situ MS analyses of protein content and specialized metabolite production to allow for facile discrimination of closely related bacterial colonies. Significance Mass spectrometry is a powerful technique that has been used to identify bacteria by their protein content and to assess bacterial functional traits through analysis of their specialized metabolites. However, until now these analyses have operated independently, which has resulted in the inability to rapidly connect bacterial phylogenetic identity with potential environmental function. To bridge this gap, we designed a MALDI-TOF mass spectrometry data acquisition and bioinformatics pipeline (IDBac) to integrate data from both intact protein and specialized metabolite spectra directly from bacterial cells grown on agar. This technique organizes bacteria into highly similar phylogenetic groups and allows for comparison of metabolic differences of hundreds of isolates in just a few hours.