PeakForest: a multi-platform digital infrastructure for interoperable metabolite spectral data and metadata management.

INTRODUCTION: Accuracy of feature annotation and metabolite identification in biological samples is a key element in metabolomics research. However, the annotation process is often hampered by the lack of spectral reference data in experimental conditions, as well as logistical difficulties in the spectral data management and exchange of annotations between laboratories. OBJECTIVES: To design an open-source infrastructure allowing hosting both nuclear magnetic resonance (NMR) and mass spectra (MS), with an ergonomic Web interface and Web services to support metabolite annotation and laboratory data management. METHODS: We developed the PeakForest infrastructure, an open-source Java tool with automatic programming interfaces that can be deployed locally to organize spectral data for metabolome annotation in laboratories. Standardized operating procedures and formats were included to ensure data quality and interoperability, in line with international recommendations and FAIR principles. RESULTS: PeakForest is able to capture and store experimental spectral MS and NMR metadata as well as collect and display signal annotations. This modular system provides a structured database with inbuilt tools to curate information, browse and reuse spectral information in data treatment. PeakForest offers data formalization and centralization at the laboratory level, facilitating shared spectral data across laboratories and integration into public databases. CONCLUSION: PeakForest is a comprehensive resource which addresses a technical bottleneck, namely large-scale spectral data annotation and metabolite identification for metabolomics laboratories with multiple instruments. PeakForest databases can be used in conjunction with bespoke data analysis pipelines in the Galaxy environment, offering the opportunity to meet the evolving needs of metabolomics research. Developed and tested by the French metabolomics community, PeakForest is freely-available at https://github.com/peakforest .

Global Profiling of Toxicologically Relevant Metabolites in Urine: Case Study of Reactive Aldehydes.

Among the numerous unknown metabolites representative of our exposure, focusing on toxic compounds should provide more relevant data to link exposure and health. For that purpose, we developed and applied a global method using data independent acquisition (DIA) in mass spectrometry to profile specifically electrophilic compounds originating metabolites. These compounds are most of the time toxic, due to their chemical reactivity toward nucleophilic sites present in biomacromolecules. The main line of cellular defense against these electrophilic molecules is conjugation to glutathione, then metabolization into mercapturic acid conjugates (MACs). Interestingly, MACs display a characteristic neutral loss in MS/MS experiments that makes it possible to detect all the metabolites displaying this characteristic loss, thanks to the DIA mode, and therefore to highlight the corresponding reactive metabolites. As a proof of concept, our workflow was applied to the toxicological issue of the oxidation of dietary polyunsaturated fatty acids, leading in particular to the formation of toxic alkenals, which lead to MACs upon glutathione conjugation and metabolization. By this way, dozens of MACs were detected and identified. Interestingly, multivariate statistical analyses carried out only on extracted HRMS signals of MACs yield a better characterization of the studied groups compared to results obtained from a classic untargeted metabolomics approach.

Combination of Isotope Labeling and Molecular Networking of Tandem Mass Spectrometry Data To Reveal 69 Unknown Metabolites Produced by Penicillium nordicum.

The secondary metabolome of Penicillium nordicum is poorly documented despite its frequent detection on contaminated food and its capacity to produce toxic metabolites such as ochratoxin A. To characterize metabolites produced by this fungi, we combined a full stable isotopes labeling with the dereplication of tandem mass spectrometry (MS/MS) data by molecular networking. First, the untargeted metabolomic analysis by high-resolution mass spectrometry of a double stable isotope labeling of P. nordicum enabled the specific detection of its metabolites and the unambiguous determination of their elemental composition. Analyses showed that infection of substrate by P. nordicum lead to the production of at least 92 metabolites and that 69 of them were completely unknown. Then, curated molecular networks of MS/MS data were generated with GNPS and MetGem, specifically on the features of interest, which allowed highlighting 13 fungisporin-related metabolites that had not previously been identified in this fungus and 8 that had never been observed in any fungus. The structures of the unknown compounds, namely, a native fungisporin and seven linear peptides, were characterized by tandem mass spectrometry experiments. The analysis of P. nordicum growing on its natural substrates, i.e. pork ham, turkey ham, and cheese, demonstrated that 10 of the known fungisporin-related metabolites and three of the new metabolites were also synthesized. Thus, the curation of data for molecular networking using a specific detection of metabolites of interest with stable isotopes labeling allowed the discovery of new metabolites produced by the food contaminant P. nordicum.