Abstract
Covering: up to the end of 2020Recently introduced computational metabolome mining tools have started to positively impact the chemical and biological interpretation of untargeted metabolomics analyses. We believe that these current advances make it possible to start decomposing complex metabolite mixtures into substructure and chemical class information, thereby supporting pivotal tasks in metabolomics analysis including metabolite annotation, the comparison of metabolic profiles, and network analyses. In this review, we highlight and explain key tools and emerging strategies covering 2015 up to the end of 2020. The majority of these tools aim at processing and analyzing liquid chromatography coupled to mass spectrometry fragmentation data. We start with defining what substructures are, how they relate to molecular fingerprints, and how recognizing them helps to decompose complex mixtures. We continue with chemical classes that are based on the presence or absence of particular molecular scaffolds and/or functional groups and are thus intrinsically related to substructures. We discuss novel tools to mine substructures, annotate chemical compound classes, and create mass spectral networks from metabolomics data and demonstrate them using two case studies. We also review and speculate about the opportunities that NMR spectroscopy-based metabolome mining of complex metabolite mixtures offers to discover substructures and chemical classes. Finally, we will describe the main benefits and limitations of the current tools and strategies that rely on them, and our vision on how this exciting field can develop toward repository-scale-sized metabolomics analyses. Complementary sources of structural information from genomics analyses and well-curated taxonomic records are also discussed. Many research fields such as natural products discovery, pharmacokinetic and drug metabolism studies, and environmental metabolomics increasingly rely on untargeted metabolomics to gain biochemical and biological insights. The here described technical advances will benefit all those metabolomics disciplines by transforming spectral data into knowledge that can answer biological questions.
Highlights
Molecular networking with electron impact (EI) ionization Mass spectrometry (MS) data Annotating the metabolite features in the network Spectral library matching Structure libraries for structural annotation MetWork MolNetEnhancer Substructure discovery by nuclear magnetic resonance (NMR) Linking spectral features to substructures dqfCOSY: generation of partial-structures from crosspeaks and pattern recognition Heteronuclear multiple-bond correlation (HMBC) barcoding HMBC networking
We show how substructure and network-based metabolomics approaches can cause a paradigm shi in the annotation level of these yet unknown metabolites in the forthcoming years by leveraging structural, chemical compound class, and substructural information from MS/MS and NMR spectral data
We expect that the linking of MS/MS spectra to information obtained from genome mining positively contributes to the annotation power of metabolomics data
Summary
Complex metabolite mixtures are found everywhere in and around us. Whether you study plant or microbial extracts, environmental samples, or human urine or plasma, these samples include vast numbers of chemically diverse molecules whose structures are mostly unknown up to date.[1,2] such molecules can play important physiological, biochemical, ecological, or diagnostic roles: in plants and microbes, they can serve as messengers or as antibacterial or antifungal agents, whereas in human bio uids molecules can be signaling molecules, biomarkers of disease, or markers of food intake or microbial activity. To facilitate the data preprocessing, processing, analysis, and interpretation numerous computational metabolomics tools have been introduced.[7] Whilst spectral databases containing reference spectra are growing for both MS and NMR, the matching rates for specialized metabolites to assign complete structures to spectral data remain low.[15] we here argue that substructure-based metabolomics work ows offer an interesting and feasible alternative since they target smaller parts of the molecules that are typically easier to structurally annotate. Basic building blocks such as saccharides and specialized metabolite scaffolds are expected to produce the same or similar spectral signals even if they are present across different complete structures It is this hypothesis that most of the currently available substructure discovery-based and chemical class-based metabolomics work ows use. We will nish with our perspective on how substructure and networkbased analyses will transform future metabolomics work ows to make them more scalable, more reliable, and allow for increased structural and functional interpretation of complex metabolite mixtures
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
