Metabolic reaction network-based recursive metabolite annotation for untargeted metabolomics

Abstract

Large-scale metabolite annotation is a challenge in liquid chromatogram-mass spectrometry (LC-MS)-based untargeted metabolomics. Here, we develop a metabolic reaction network (MRN)-based recursive algorithm (MetDNA) that expands metabolite annotations without the need for a comprehensive standard spectral library. MetDNA is based on the rationale that seed metabolites and their reaction-paired neighbors tend to share structural similarities resulting in similar MS2 spectra. MetDNA characterizes initial seed metabolites using a small library of MS2 spectra, and utilizes their experimental MS2 spectra as surrogate spectra to annotate their reaction-paired neighbor metabolites, which subsequently serve as the basis for recursive analysis. Using different LC-MS platforms, data acquisition methods, and biological samples, we showcase the utility and versatility of MetDNA and demonstrate that about 2000 metabolites can cumulatively be annotated from one experiment. Our results demonstrate that MetDNA substantially expands metabolite annotation, enabling quantitative assessment of metabolic pathways and facilitating integrative multi-omics analysis.

A Reference Feature based method for Quantification and Identification of LC-MS based untargeted metabolomics

“…Traditional method for metabolite identification is to select one representative MS2, such as MS2 corresponding to the maximum precursor’s intensity, and assign a hard threshold to filter possible false discovery 6,10 . In RFQI, all MS2 derived from a large number of samples and mapped to one feature are applied for this feature’s metabolite identification, which can increase the possibilities of mapping them to library MS2.…”

“…Besides, the poor efficiency of metabolite identification based on MS2 is another bottleneck of LC-MS based untargeted metabolomics. It is mainly due to the limited number of known standards 6 which is outside of our study aim, and the constraint number of collected MS2 with high quality, especially in data dependent acquisition (DDA) mode, in which only part of selected precursors is dissociated in one scan. Data-independent acquisition (DIA) mode can dissociate all precursors in one scan, but it increases difficulties in MS2 extraction of precursors and identification 6 .…”

Molecular networking as a dereplication strategy for monitoring metabolites of natural product treated cancer cells

“…The assignment was unambiguous according to the tandem mass analysis (Figure B), and subsequently guided us to the identification of the other yellow node in the same networking cluster. Such a strategy was recently used to identify reaction‐paired neighbor metabolites in cells . From its tandem mass spectral features (Figure B), the second yellow node was then assigned to cordycepin diphosphate (CDP) ( m / z 412.0415, [M + H] + ), revealing that cordycepin, similar to its adenosine analogue, was readily transformed to its phosphorylated version.…”

“…Recently, a dereplication strategy utilizing Global Natural Product Social (GNPS) molecular networking has been proposed to leverage the structural features of known compounds to decrypt the tandem mass spectra of unknown compounds . Specifically, cosine similarities among each pair of tandem mass spectra of individual specialized metabolites are calculated, of which the compounds with similar chemical structures are prone to cluster together . This method provides an ability to rapidly categorize hundreds or thousands of specialized metabolomes collected from various organisms based on their chemical structures.…”

“…[14][15][16][17] Specifically, cosine similarities among each pair of tandem mass spectra of individual specialized metabolites are calculated, of which the compounds with similar chemical structures are prone to cluster together. 17,18 This method provides an ability to rapidly categorize hundreds or thousands of specialized metabolomes collected from various organisms based on their chemical structures. In this regard, an unknown compound spectrally matched to a structurally annotated compound is resolved as well as elucidated in a more logical fashion.…”