ReDU: a framework to find and reanalyze public mass spectrometry data.

We present ReDU ( https://redu.ucsd.edu/ ), a system for metadata capture of public mass spectrometry-based metabolomics data, with validated controlled vocabularies. Systematic capture of knowledge enables the reanalysis of public data and/or co-analysis of one's own data. ReDU enables multiple types of analyses, including finding chemicals and associated metadata, comparing the shared and different chemicals between groups of samples, and metadata-filtered, repository-scale molecular networking.

Protocol for community-created public MS/MS reference spectra within the Global Natural Products Social Molecular Networking infrastructure.

RATIONALE: A major hurdle in identifying chemicals in mass spectrometry experiments is the availability of tandem mass spectrometry (MS/MS) reference spectra in public databases. Currently, scientists purchase databases or use public databases such as Global Natural Products Social Molecular Networking (GNPS). The MSMS-Chooser workflow is an open-source protocol for the creation of MS/MS reference spectra directly in the GNPS infrastructure. METHODS: An MSMS-Chooser Sample Template is provided and completed manually. The MSMS-Chooser Submission File and Sequence Table for data acquisition were programmatically generated. Standards from the Mass Spectrometry Metabolite Library (MSMLS) suspended in a methanol-water (1:1) solution were analyzed. Flow injection on an LC/MS/MS system was used to generate negative and positive mode data using data-dependent acquisition. The MS/MS spectra and Submission File were uploaded to MSMS-Chooser workflow in GNPS for automatic selection of MS/MS spectra. RESULTS: Data acquisition and processing required ~2 h and ~2 min, respectively, per 96-well plate using MSMS-Chooser. Analysis of the MSMLS, over 600 small molecules, using MSMS-Chooser added 889 spectra (including multiple adducts) to the public library in GNPS. Manual validation of one plate indicated accurate selection of MS/MS scans (true positive rate of 0.96 and a true negative rate of 0.99). The MSMS-Chooser output includes a table formatted for inclusion in the GNPS library as well as the ability to directly launch searches via MASST. CONCLUSIONS: MSMS-Chooser enables rapid data acquisition, data analysis (selection of MS/MS spectra), and a formatted table for inspection and upload to GNPS. Open file-format data (.mzML or.mzXML) from most mass spectrometry platforms containing MS/MS spectra can be processed using MSMS-Chooser. MSMS-Chooser democratizes the creation of MS/MS reference spectra in GNPS which will improve annotation and strengthen the tools which use the annotation information.

Enhancing untargeted metabolomics using metadata-based source annotation.

Human untargeted metabolomics studies annotate only ~10% of molecular features. We introduce reference-data-driven analysis to match metabolomics tandem mass spectrometry (MS/MS) data against metadata-annotated source data as a pseudo-MS/MS reference library. Applying this approach to food source data, we show that it increases MS/MS spectral usage 5.1-fold over conventional structural MS/MS library matches and allows empirical assessment of dietary patterns from untargeted data.

Effects of Immunization With the Soil-Derived Bacterium Mycobacterium vaccae on Stress Coping Behaviors and Cognitive Performance in a "Two Hit" Stressor Model.

Previous studies demonstrate that Mycobacterium vaccae NCTC 11659 (M. vaccae), a soil-derived bacterium with anti-inflammatory and immunoregulatory properties, is a potentially useful countermeasure against negative outcomes to stressors. Here we used male C57BL/6NCrl mice to determine if repeated immunization with M. vaccae is an effective countermeasure in a "two hit" stress exposure model of chronic disruption of rhythms (CDR) followed by acute social defeat (SD). On day -28, mice received implants of biotelemetric recording devices to monitor 24-h rhythms of locomotor activity. Mice were subsequently treated with a heat-killed preparation of M. vaccae (0.1 mg, administered subcutaneously on days -21, -14, -7, and 27) or borate-buffered saline vehicle. Mice were then exposed to 8 consecutive weeks of either stable normal 12:12 h light:dark (LD) conditions or CDR, consisting of 12-h reversals of the LD cycle every 7 days (days 0-56). Finally, mice were exposed to either a 10-min SD or a home cage control condition on day 54. All mice were exposed to object location memory testing 24 h following SD. The gut microbiome and metabolome were assessed in fecal samples collected on days -1, 48, and 62 using 16S rRNA gene sequence and LC-MS/MS spectral data, respectively; the plasma metabolome was additionally measured on day 64. Among mice exposed to normal LD conditions, immunization with M. vaccae induced a shift toward a more proactive behavioral coping response to SD as measured by increases in scouting and avoiding an approaching male CD-1 aggressor, and decreases in submissive upright defensive postures. In the object location memory test, exposure to SD increased cognitive function in CDR mice previously immunized with M. vaccae. Immunization with M. vaccae stabilized the gut microbiome, attenuating CDR-induced reductions in alpha diversity and decreasing within-group measures of beta diversity. Immunization with M. vaccae also increased the relative abundance of 1-heptadecanoyl-sn-glycero-3-phosphocholine, a lysophospholipid, in plasma. Together, these data support the hypothesis that immunization with M. vaccae stabilizes the gut microbiome, induces a shift toward a more proactive response to stress exposure, and promotes stress resilience.

Reproducible molecular networking of untargeted mass spectrometry data using GNPS.

Global Natural Product Social Molecular Networking (GNPS) is an interactive online small molecule-focused tandem mass spectrometry (MS2) data curation and analysis infrastructure. It is intended to provide as much chemical insight as possible into an untargeted MS2 dataset and to connect this chemical insight to the user's underlying biological questions. This can be performed within one liquid chromatography (LC)-MS2 experiment or at the repository scale. GNPS-MassIVE is a public data repository for untargeted MS2 data with sample information (metadata) and annotated MS2 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 MS2 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.

Untargeted mass spectrometry-based metabolomics approach unveils molecular changes in raw and processed foods and beverages.

In our daily lives, we consume foods that have been transported, stored, prepared, cooked, or otherwise processed by ourselves or others. Food storage and preparation have drastic effects on the chemical composition of foods. Untargeted mass spectrometry analysis of food samples has the potential to increase our chemical understanding of these processes by detecting a broad spectrum of chemicals. We performed a time-based analysis of the chemical changes in foods during common preparations, such as fermentation, brewing, and ripening, using untargeted mass spectrometry and molecular networking. The data analysis workflow presented implements an approach to study changes in food chemistry that can reveal global alterations in chemical profiles, identify changes in abundance, as well as identify specific chemicals and their transformation products. The data generated in this study are publicly available, enabling the replication and re-analysis of these data in isolation, and serve as a baseline dataset for future investigations.