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Determining mechanism of action (MOA) is one of the biggest challenges in natural products discovery. Here, we report a comprehensive platform that uses Similarity Network Fusion (SNF) to improve MOA predictions by integrating data from the cytological profiling high-content imaging platform and the gene expression platform Functional Signature Ontology, and pairs these data with untargeted metabolomics analysis for de novo bioactive compound discovery. The predictive value of the integrative approach was assessed using a library of target-annotated small molecules as benchmarks. Using Kolmogorov-Smirnov (KS) tests to compare in-class to out-of-class similarity, we found that SNF retains the ability to identify significant in-class similarity across a diverse set of target classes, and could find target classes not detectable in either platform alone. This confirmed that integration of expression-based and image-based phenotypes can accurately report on MOA. Furthermore, we integrated untargeted metabolomics of complex natural product fractions with the SNF network to map biological signatures to specific metabolites. Three examples are presented where SNF coupled with metabolomics was used to directly functionally characterize natural products and accelerate identification of bioactive metabolites, including the discovery of the azoxy-containing biaryl compounds parkamycins A and B. Our results support SNF integration of multiple phenotypic screening approaches along with untargeted metabolomics as a powerful approach for advancing natural products drug discovery.

The composition of the skin microbiota varies widely among individuals when sampled at the same body site. A key question is which molecular factors determine strain-level variability within sub-ecosystems of the skin microbiota. Here, we used a genomics-guided approach to identify an antibacterial biosynthetic gene cluster in Cutibacterium acnes (formerly Propionibacterium acnes), a human skin commensal bacterium that is widely distributed across individuals and skin sites. Experimental characterization of this biosynthetic gene cluster resulted in identification of a new thiopeptide antibiotic, cutimycin. Analysis of individual human skin hair follicles revealed that cutimycin contributed to the ecology of the skin hair follicle microbiota and helped to reduce colonization of skin hair follicles by Staphylococcus species.

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.

Nuclear magnetic resonance (NMR) spectroscopy is a powerful analytical technique with the ability to acquire both quantitative and structurally insightful data for multiple components in a test sample. This makes NMR spectroscopy a desirable tool to understand, monitor, and optimize chemical transformations. While quantitative NMR (qNMR) approaches relying on internal standards are well‐established, using an absolute external calibration scheme is beneficial for reaction monitoring as resonance overlap complications from an added reference material to the sample can be avoided. Particularly, this type of qNMR technique is of interest with benchtop NMR spectrometers as the likelihood of resonance overlap is only enhanced with the lower magnetic field strengths of the used permanent magnets. The included study describes a simple yet robust methodology to determine concentration conversion factors for NMR systems using single‐ and multi‐analyte linear regression models. This approach is leveraged to investigate a pharmaceutically relevant amide coupling batch reaction. An on‐line stopped‐flow (i.e., interrupted‐flow or paused‐flow) benchtop NMR system was used to monitor both the 1,1′‐carbonyldiimidazole (CDI) promoted acid activation and the amide coupling. The results highlight how quantitative measurements in benchtop NMR systems can provide valuable information and enable analysts to make decisions in real time. [ABSTRACT FROM AUTHOR]

Water or moisture content in human stool samples is an important parameter for bioanalytical and clinical purposes. For bioanalytical use, accurate quantitation of water content in stool can provide the extent of dilution within the stool sample which can further be used for absolute quantitation of various stool based biomarkers. For clinical use, water or moisture content in stool is an important indicator of gastrointestinal health, and its accurate determination can enable quantitative assessment of the Bristol Stool Form Scale. In general, accurate determination of water content of stool samples is cumbersome, low-throughput process and is prone to harmful stool pathogens biocontamination, sample cross-contamination using techniques such as gravimetry and karl fischer titration. Here, we report a novel user-friendly high-throughput method to quantitatively and accurately measure the overall water content in human fecal samples nondestructively and biocontained in a closed tube using benchtop a 1 H time domain nuclear magnetic resonance analyzer. We used gravimetry and measurement of various bile acid metabolites in stool to verify the accuracy and robustness of the water content measurement using this technique.