How many metabolites compose the metabolome? What’s their origin and fate? What’s the functional role of these molecules in health and disease? Many technological advancements are still required to address these and other intriguing questions in metabolism research. In particular, dealing with the correct assignment of thousands of metabolic signals from metabolome-wide studies currently requires a time-consuming workflow, with the characterization of metabolic unknowns being the narrow bottleneck to move through. Here we discuss the state-of-the-art and future of mass spectrometry-based technologies and bioinformatic tools for metabolite discovery and identification, and how these can impact the discovery of metabolites that actively modulate the phenotype. Specifically, we will focus on the current efforts for streamlining metabolite ID for metabolomics activity screening (MAS), by addressing five main areas: i) increasing the coverage of experimental MS/MS databases for known unknowns (i.e., METLIN), ii) automating the identification of known metabolites from global metabolomics studies (i.e., MISA); iii) streamlining pathway interpretation and meta-analysis at systems-level (i.e., Meta XCMS Online); iv) generating structural hypothesis for deciphering unknown unknowns (i.e. Fragment Similarity Search); and v) the emerging analytical strategies for the detection of highly-active low-abundant metabolites and their structural confirmation. Taken together our examples showcase global metabolomics as a powerful approach for metabolite activity discovery in biological systems.

Metabolite identification and unknown characterization for metabolomics activity screening

2019 Fall National Meeting

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