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.

Human untargeted metabolomics studies succeed in annotating only ~10% of molecular features. We, therefore, introduce reference data-driven analysis that uses the source data as a pseudo-MS/MS reference library to match against human metabolomics MS/MS data. We demonstrate this approach with food source data, allowing an empirical assessment of dietary patterns from untargeted data but is broadly applicable and provides an additional layer of interpretability to metabolomics data.

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.

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.

Rationale A major hurdle in identifying chemicals in mass spectrometry experiments is the availability of MS/MS reference spectra in public databases. Currently, scientists purchase databases or use public databases such as GNPS. The MSMS-Chooser workflow empowers the creation of MS/MS reference spectra directly in the GNPS infrastructure.Methods An MSMS-Chooser sample template was completed with the required information and sequence tables were generated programmatically. Standards in methanol-water (1:1) solution (1 μM) were placed into wells individually. An LC-MS/MS system using data-dependent acquisition in positive and negative modes was used. Species that may be generated under typical ESI conditions are chosen. The MS/MS spectra and MSMS-Chooser sample template were subsequently uploaded to MSMS-Chooser in GNPS for automatic MS/MS spectral annotation.Results Data acquisition quickly and effectively collected MS/MS spectra. MSMS-Chooser was able to accurately annotate 99.2% of the manually validated MS/MS scans that were generated from the chemical standards. The output of MSMS-Chooser includes a table ready for inclusion in the GNPS library (after inspection) as well as the ability to directly launch searches via MASST. Altogether, the data acquisition, processing, and upload to GNPS took ~2 hours for our proof-of-concept results.Conclusions The MSMS-Chooser workflow enables the rapid data acquisition, analysis, and annotation of chemical standards, and uploads the MS/MS spectra to community-driven GNPS. 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.

Herein, we present a protocol for the use of Global Natural Products Social (GNPS) Molecular Networking, an interactive online chemistry-focused mass spectrometry data curation and analysis infrastructure. The goal of GNPS is to provide as much chemical insight for an untargeted tandem mass spectrometry data set as possible and to connect this chemical insight to the underlying biological questions a user wishers to address. This can be performed within one experiment or at the repository scale. GNPS not only serves as a public data repository for untargeted tandem mass spectrometry data with the sample information (metadata), it also captures community knowledge that is disseminated via living data across all public data. One or the main analysis tools used by the GNPS community is molecular networking. Molecular networking creates a structured data table that reflects the chemical space from tandem mass spectrometry experiments via computing the relationships of the tandem mass spectra through spectral similarity. This protocol provides step-by-step instructions for creating reproducible high-quality molecular networks. For training purposes, the reader is led through the protocol from recalling a public data set and its sample information 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.

Summary The human metabolome has remained largely unknown, with most studies annotating ∼10% of features. In nucleic acid sequencing, annotating transcripts by source has proven essential for understanding gene function. Here we generalize this concept to stool, plasma, urine and other human metabolomes, discovering that food-based annotations increase the interpreted fraction of molecular features 7-fold, providing a general framework for expanding the interpretability of human metabolomic “dark matter.”

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.

Metabolomics data are difficult to find and reuse, even in public repositories. We, therefore, developed the Reanalysis of Data User (ReDU) interface (<https://redu.ucsd.edu/>), a community- and data-driven approach that solves this problem at the repository scale. ReDU enables public data discovery and co- or re-analysis via uniformly formatted, publicly available MS/MS data and metadata in the Global Natural Product Social Molecular Networking Platform (GNPS), consistent with findable, accessible, interoperable, and reusable (FAIR) principles.[1][1] [1]: #ref-1

In the new era of personalized modern medicine, more “precise” and diverse concepts of wellness and approaches in the development and maintenance of good health will sooner or later apply to cosmetic surgery and the field of cosmetology in general. For example, a relatively new field of research on gut and skin microbiome and its understanding provides insight into the microbial involvement in human skin physiology and related disorders [1,2]. Based on current research, evidence, and trends, an extrapolation could be made that secreted chemicals and bacteria within the human gut have a drastic effect on skin appearance and its aging [3,4]. A discussion on the extent of the impact of gut microbiome on human health and how it can be used to advance the field of cosmetology and good health is timely.