This is the first work that effectively integrates orthogonal annotation methods and MS2-based fragmentation studies to improve metabolite annotation in urine samples. The exhaustive MS2-based annotation outperformed similar studies applied to larger cohorts while offering the discovery of metabolites not identified by the MS2 library search. 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. Using a combination of unsupervised substructure discovery (MS2LDA), the in silico tool network annotation propagation (NAP), and ClassyFire chemical ontology, embedded in a multilayered molecular network by MolNetEnhancer, we were able to expand the chemical characterization to ∼50% of the data set.
Molecular networking based on library matching resulted in the annotation of up to 62 urinary compounds.
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 a high fragmentation score (cosine similarity ≥ 0.7), that is, ∼2% of mass features containing MS2 spectra. The samples (n = 10) were analyzed by hydrophilic interaction chromatography-quadrupole time-of-flight mass spectrometry in both electrospray ionization (ESI) modes.
Here, we investigated the use of tandem MS-based enhanced molecular networks (MolNetEnhancer) to improve the metabolite annotation of urine extracts. Although global liquid chromatography-mass spectrometry (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. 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.
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