44ª Reunião Anual Virtual da SBQ

The urine metabolome constitutes a rich source of functional information reflecting physiological states that are influenced by distinct conditions and biological stresses, such as responses to drug treatments or disease manifestations. Although global LC-MS profiling provides the most comprehensive measurement of metabolites in complex biological samples, annotation remains a challenge, and computational approaches are necessary to translate the molecular composition into biological knowledge. Here, we investigated the use of tandem MS-based enhanced molecular networks (MolNetEnhancer) to improve the metabolite annotation of urine extracts. The samples were analyzed by hydrophilic interaction chromatography (HILIC)-QTOF mass spectrometry in both electrospray (ESI) ionization modes. Consistent with other common data preprocessing software, the use of Progenesis QI led to the annotation of up to 20 metabolites based on MS2 library searches showing high fragmentation score (cosine similarity ≥ 0.7), i.e., ~2% of mass features containing MS2 spectra. Molecular networking (MN) based on library match resulted in annotation of up to 62 urinary compounds. Using a combination of unsupervised substructure discovery (MS2LDA), the in silico tool network annotation propagation (NAP), and ClassyFire chemical ontology, embedded in a multi-layered MN by MolNetEnhancer, we were able to expand the chemical characterization to ~50% of the dataset. The integrative approach led to the annotation of 275 compounds at the Metabolomics Standards Initiative (MSI) confidence level 2, as well as 459 and 578 urinary metabolites (MSI level 3) in both negative and positive ESI modes, respectively. The exhaustive MS2-based annotation outperformed similar studies applied to larger cohorts while offering the discovery of metabolites not identified by MS2 library search. This is the first work that effectively integrates orthogonal annotation methods and MS2-based fragmentation studies to improve metabolite annotation in urine samples.