Metabolite annotation from knowns to unknowns through knowledge-guided multi-layer metabolic networking

Abstract

Liquid chromatography - mass spectrometry (LC-MS) based untargeted metabolomics allows to measure both known and unknown metabolites in the metabolome. However, unknown metabolite annotation is a major challenge in untargeted metabolomics. Here, we develop an approach, namely, knowledge-guided multi-layer network (KGMN), to enable global metabolite annotation from knowns to unknowns in untargeted metabolomics. The KGMN approach integrates three-layer networks, including knowledge-based metabolic reaction network, knowledge-guided MS/MS similarity network, and global peak correlation network. To demonstrate the principle, we apply KGMN in an in vitro enzymatic reaction system and different biological samples, with ~100–300 putative unknowns annotated in each data set. Among them, >80% unknown metabolites are corroborated with in silico MS/MS tools. Finally, we validate 5 metabolites that are absent in common MS/MS libraries through repository mining and synthesis of chemical standards. Together, the KGMN approach enables efficient unknown annotations, and substantially advances the discovery of recurrent unknown metabolites for common biological samples from model organisms, towards deciphering dark matter in untargeted metabolomics.

A mass spectrum-oriented computational method for ion mobility-resolved untargeted metabolomics

“…The preparation of biological samples followed our published protocols 34 , 43 . For NIST human urine samples, 150 μL of urine was taken and 600 μL of MeOH was added for extraction.…”

“…The rest of the procedure was kept the sample as described for NIST human urine samples. The preparation of mouse liver and brain tissues followed our previous publication 34 , 43 . The natural products were dissolved in MeOH with a concentration of 1 mg/mL as the stock solution.…”

“…The m/z tolerance was set as 20 ppm in MS1 match. RTs in the metabolite library were first recalibrated into the experimental condition according to the protocol in our previous publication 43 , 52 , then matched to experimental values of 4D features. Finally, CCS match was performed.…”

Plant-Derived Products with Therapeutic Potential against Gastrointestinal Bacteria

“…Identification of metabolites from a variety of sources, including leaves, roots, soils, and volatiles, has led to the identification of biosynthesis pathways by identifying key shifts in the metabolite profile. While full metabolite scans with chromatography and mass spectrometry (MS1 mode) are (more accurately) quantified, they are unreliable in metabolite annotation, since several compounds can have the same mass but have different molecular formulas or may have the same molecular formula but differ in their chemical structure [ 133 , 137 ]. To address this challenge, metabolites are further fragmented using tandem MS mode (also known as MS2 or MS/MS or MSn when deeper fragmentation levels are included).…”

“…To address this challenge, metabolites are further fragmented using tandem MS mode (also known as MS2 or MS/MS or MSn when deeper fragmentation levels are included). With the aid of a variety of software and tools that have been developed for mining and annotating MS2 fragmentation data, metabolites can be structurally annotated and readily identified [ 137 ]. Aside from developing new classes of antibiotics with direct growth inhibition effects against bacteria, future research should also focus on finding indirect antimicrobials that are more effective and have better safety profiles.…”