xMSannotator: An R Package for Network-Based Annotation of High-Resolution Metabolomics Data

Abstract

Improved analytical technologies and data extraction algorithms enable detection of >10,000 reproducible signals by liquid chromatography high-resolution mass spectrometry, creating a bottleneck in chemical identification. In principle, measurement of more than one million chemicals would be possible if algorithms were available to facilitate utilization of the raw mass spectrometry data, especially low abundance metabolites. Here we describe an automated computational framework to annotate ions for possible chemical identity using a multistage clustering algorithm in which metabolic pathway associations are used along with intensity profiles, retention time characteristics, mass defect, and isotope/adduct patterns. The algorithm uses high-resolution mass spectrometry data for a series of samples with common properties and publicly available chemical, metabolic and environmental databases to assign confidence levels to annotation results. Evaluation results show that the algorithm achieves an F1-measure of 0.8 for a dataset with known targets and is more robust than previously reported results for cases when database size is much greater than the actual number of metabolites. MS/MS evaluation of a set of randomly selected 210 metabolites annotated using xMSannotator in an untargeted metabolomics human dataset shows that 80% of features with high or medium confidence scores have ion dissociation patterns consistent with the xMSannotator annotation. The algorithm has been incorporated into an R package, xMSannotator, which includes utilities for querying local or online databases such as ChemSpider, KEGG, HMDB, T3DB, and LipidMaps.

Putrescine as indicator of manganese neurotoxicity: Dose-response study in human SH-SY5Y cells

“…To test for metabolic precursors or products variation in association with putrescine, we used xMSannotator (Uppal et al, 2017) to select mass spectral features with accurate mass matches (10 ppm) to metabolites in four central pathways of putrescine-associated metabolism [polyamine metabolism, methionine (Met) metabolism, urea cycle and neurotransmitter GABA shunt] in HMDB and KEGG (Supplemental Table 1). Ninety-six matches were obtained for 62 metabolites, with more than half positively correlated and a smaller number negatively correlated with putrescine (Supplemental Table 1).…”

“…To help overcome the false discovery due to multiple comparisons, the experiments were designed with nine independent biological replicates for each of the six experimental conditions of Mn concentration. Metabolic extracts were prepared as described previously (Go et al, 2014, 2015a; Soltow et al, 2013; Uppal et al, 2013, 2016, 2017). Briefly, after the 5-h treatment, cells were washed with phosphate-buffered saline and 200 μL of 1:2 HPLC grade water: acetonitrile solution containing a mixture of stable isotopic standards (Go et al, 2015a; Soltow et al, 2013) was added to each plate to precipitate proteins and extract metabolites (Go et al, 2014).…”

“…For other metabolites, Level 5 identification (Schymanski et al, 2014) was made using Kyoto Encyclopedia of Genes and Genomes (KEGG, http://www.genome.jp/kegg/) (Kanehisa et al, 2017) and Metlin (Smith et al, 2005) mass spectrometry databases at 5 ppm tolerance, with confidence scores for annotation obtained using xMSannotator (Uppal et al, 2017) with Human Metabolome DataBase (HMDB) version 3.5 http://www.hmdb.ca/) (Wishart et al, 2013). Assignment of the chemical identity and level identification for the significant metabolites was made using the criteria of Schymanski et al (2014).…”

Annotation: A Computational Solution for Streamlining Metabolomics Analysis

“…Finally, this approach also needs a correlation threshold to be determined. Variations on these approach include RAMClust [28], that uses an hierarchical clustering-based approach to group both MS and MS/MS peaks, or xMSannotator [29], which uses a weighted correlation network analysis and does not require a minimum correlation threshold to be defined.…”

“…An extension of this methodology is provided by computational tools such as MI-Pack [41], mummichog [42], ProbMetab [43], xMSannotator [29] or XCMS [44], among others [45, 46]. These tools use biochemical pathways to filter and rank lists of putative identifications obtained after accurate mass search, increasing the likelihood of obtaining correct putative metabolite identifications.…”