Combining Feature-Based Molecular Networking and Contextual Mass Spectral Libraries to Decipher Nutrimetabolomics Profiles

Abstract

Untargeted metabolomics approaches deal with complex data hindering structural information for the comprehensive analysis of unknown metabolite features. We investigated the metabolite discovery capacity and the possible extension of the annotation coverage of the Feature-Based Molecular Networking (FBMN) approach by adding two novel nutritionally-relevant (contextual) mass spectral libraries to the existing public ones, as compared to widely-used open-source annotation protocols. Two contextual mass spectral libraries in positive and negative ionization mode of ~300 reference molecules relevant for plant-based nutrikinetic studies were created and made publicly available through the GNPS platform. The postprandial urinary metabolome analysis within the intervention of Vaccinium supplements was selected as a case study. Following the FBMN approach in combination with the added contextual mass spectral libraries, 67 berry-related and human endogenous metabolites were annotated, achieving a structural annotation coverage comparable to or higher than existing non-commercial annotation workflows. To further exploit the quantitative data obtained within the FBMN environment, the postprandial behavior of the annotated metabolites was analyzed with Pearson product-moment correlation. This simple chemometric tool linked several molecular families with phase II and phase I metabolism. The proposed approach is a powerful strategy to employ in longitudinal studies since it reduces the unknown chemical space by boosting the annotation power to characterize biochemically relevant metabolites in human biofluids.

Reproducible mass spectrometry data processing and compound annotation in MZmine 3

“…▲CRITICAL Public spectral libraries contain mass spectra acquired under a wide range of instrumental conditions and using a wide range of sample preparation and data curation protocols. 50 As a consequence, spectra can vary greatly in terms of observed mass fragments, intensity ratios, and spectral quality. 51 For this reason, we recommend the users to treat annotation results with caution when public data repositories are used for automated spectral matching.…”

The Impact of the Culture Regime on the Metabolome and Anti-Phytopathogenic Activity of Marine Fungal Co-Cultures

“…Based on the yields and activity profiles, a comparative untargeted metabolomics analysis using the Feature-Based Molecular Networking (FBMN) [22] tool was performed on the mono-and co-cultures of P. influorescens and P. nobilis obtained from the continuously shaken (SH) liquid PDB, CDB, and SDB media. The aim was to infer chemical variations and molecular families that may underlie the differential bioactivities observed.…”

Reliable cross-ion mode chemical similarity prediction between MS2spectra

“…Therefore, common comparison metrics like the cosine score are not suitable for predicting chemical similarity between spectra of two different ion modes. As a result, positive and negative ion mode mass spectra are mostly analyzed separately, for instance, by searching in separate reference libraries and creating two separate molecular networks 10, 11, 12 . Where approaches like MolNotator 13 and Ion Identity Networking 14 can merge positive and negative mode spectra into one network, they require adduct identification based on well-aligned retention times and the recognition of specific mass differences between mass features.…”