Mass Spectral Similarity Networking and Gas-Phase Fragmentation Reactions in the Structural Analysis of Flavonoid Glycoconjugates.
Alan Cesar PilonHaiwei GuDaniel RafteryVanderlan da Silva BolzaniNorberto Peporine LopesIan Castro-GamboaFausto Carnevale NetoPublished in: Analytical chemistry (2019)
Flavonoids represent an important class of natural products with a central role in plant physiology and human health. Their accurate annotation using untargeted mass spectrometry analysis still relies on differentiating similar chemical scaffolds through spectral matching to reference library spectra. In this work, we combined molecular network analysis with rules for fragment reactions and chemotaxonomy to enhance the annotation of similar flavonoid glyconjugates. Molecular network topology progressively propagated the flavonoid chemical functionalization according to collision-induced dissociation (CID) reactions, as the following chemical attributes: aglycone nature, saccharide type and number, and presence of methoxy substituents. This structure-based distribution across the spectral networks revealed the chemical composition of flavonoids across intra- and interspecies and guided the putatively assignment of 64 isomers and isobars in the Chrysobalanaceae plant species, most of which are not accurately annotated by automated untargeted MS2 matching. These proof of concept results demonstrate how molecular networking progressively grouped structurally related molecules according to their product ion scans, abundances, and ratios. The approach can be extrapolated to other classes of metabolites sharing similar structures and diagnostic fragments from tandem mass spectrometry.
Keyphrases
- mass spectrometry
- liquid chromatography
- tandem mass spectrometry
- gas chromatography
- high performance liquid chromatography
- network analysis
- human health
- high resolution
- high resolution mass spectrometry
- optical coherence tomography
- ultra high performance liquid chromatography
- risk assessment
- simultaneous determination
- ms ms
- solid phase extraction
- capillary electrophoresis
- single molecule
- machine learning
- gas chromatography mass spectrometry
- rna seq
- social media
- multiple sclerosis
- dual energy
- deep learning
- single cell
- drug induced
- high glucose
- healthcare
- magnetic resonance
- health information
- high throughput
- electron transfer