Unsupervised Discovery and Comparison of Structural Families Across Multiple Samples in Untargeted Metabolomics

Abstract

In untargeted metabolomics approaches, the inability to structurally annotate relevant features and map them to biochemical pathways is hampering the full exploitation of many metabolomics experiments. Furthermore, variable metabolic content across samples result in sparse feature matrices that are statistically hard to handle. Here, we introduce MS2LDA+ that tackles both above-mentioned problems. Previously, we presented MS2LDA, which extracts biochemically relevant molecular substructures (“Mass2Motifs”) from a collection of fragmentation spectra as sets of co-occurring molecular fragments and neutral losses, thereby recognizing building blocks of metabolomics. Here, we extend MS2LDA to handle multiple metabolomics experiments in one analysis, resulting in MS2LDA+. By linking Mass2Motifs across samples, we expose the variability in prevalence of structurally related metabolite families. We validate the differential prevalence of substructures between two distinct samples groups and apply it to fecal samples. Subsequently, within one sample group of urines, we rank the Mass2Motifs based on their variance to assess whether xenobiotic-derived substructures are among the most-variant Mass2Motifs. Indeed, we could ascribe 22 out of the 30 most-variant Mass2Motifs to xenobiotic-derived substructures including paracetamol/acetaminophen mercapturate and dimethylpyrogallol. In total, we structurally characterized 101 Mass2Motifs with biochemically or chemically relevant substructures. Finally, we combined the discovered metabolite families with full scan feature intensity information to obtain insight into core metabolites present in most samples and rare metabolites present in small subsets now linked through their common substructures. We conclude that by biochemical grouping of metabolites across samples MS2LDA+ aids in structural annotation of metabolites and guides prioritization of analysis by using Mass2Motif prevalence.

MolNetEnhancer: enhanced molecular networks by integrating metabolome mining and annotation tools

“…Shared substructures or motifs between two molecular features are visualized as multiple edges connecting the nodes. Furthermore, motifs found within a molecular feature can be visualized as pie charts, where the relative abundance of each motif represents the overlap score, a score measuring how much of the motif is present in the spectrum [62]. Furthermore, for each molecular family, the x most shared motifs are shown, where x is defined by the user.…”

In-silico Optimisation of Mass Spectrometry Fragmentation Strategies in Metabolomics

“…Beer and urine samples (labelled multi-beer and multi-urine) from a previously published study [21] are used in our experiments. Here we briefly summarise the sample preparation and analytical platform for the multi-beer and multi-urine in [21]. 19 different beers were collected from bottles over a period of 5 months and frozen immediately after sampling.…”

“…The column oven was maintained at a constant temperature of 25 • C (QE) or 40 • C (Fusion). Blank runs, quality control samples and three standard mixes were prepared according to the standard procedures at Glasgow Polyomics [21,6].…”

MESSAR: Automated recommendation of metabolite substructures from tandem mass spectra

“…These tools start by filtering the molecular database 18 using the precursor m/z of the unknown spectra, yielding up to thousands of structure 19 candidates. 20 To subsequently score and rank these candidates two categories of algorithms have 21 been proposed. First, in silico fragmentation tools simulate theoretical spectra for 22 each candidate metabolite and compare those with the query spectrum [6][7][8][9].…”

“…MS2LDA is a recent framework proposed by van der Hooft et al [20][21][22]. It de-46 composes unlabeled MS/MS spectra into patterns of co-occurring fragments and losses, 47 referred to as "Mass2Motifs", which are indicative of biological substructures.…”