Integrated Analytical and Statistical Two-Dimensional Spectroscopy Strategy for Metabolite Identification: Application to Dietary Biomarkers

Joram M Posma, Isabel Garcia-Perez, James C. Heaton, Paula Burdisso, John C. Mathers, John Draper, Matt Lewis, John C. Lindon, Gary Frost, Elaine Holmes, Jeremy K. Nicholson

Allbwn ymchwil: Cyfraniad at gyfnodolynErthygladolygiad gan gymheiriaid

40 Dyfyniadau(SciVal)
208 Wedi eu Llwytho i Lawr (Pure)

Crynodeb

A major purpose of exploratory metabolic profiling is for the identification of molecular species that are statistically associated with specific biological or medical outcomes; unfortunately, the structure elucidation process of unknowns is often a major bottleneck in this process. We present here new holistic strategies that combine different statistical spectroscopic and analytical techniques to improve and simplify the process of metabolite identification. We exemplify these strategies using study data collected as part of a dietary intervention to improve health and which elicits a relatively subtle suite of changes from complex molecular profiles. We identify three new dietary biomarkers related to the consumption of peas (N-methyl nicotinic acid), apples (rhamnitol), and onions (N-acetyl-S-(1Z)-propenyl-cysteine-sulfoxide) that can be used to enhance dietary assessment and assess adherence to diet. As part of the strategy, we introduce a new probabilistic statistical spectroscopy tool, RED-STORM (Resolution EnhanceD SubseT Optimization by Reference Matching), that uses 2D J-resolved 1H NMR spectra for enhanced information recovery using the Bayesian paradigm to extract a subset of spectra with similar spectral signatures to a reference. RED-STORM provided new information for subsequent experiments (e.g., 2D-NMR spectroscopy, solid-phase extraction, liquid chromatography prefaced mass spectrometry) used to ultimately identify an unknown compound. In summary, we illustrate the benefit of acquiring J-resolved experiments alongside conventional 1D 1H NMR as part of routine metabolic profiling in large data sets and show that application of complementary statistical and analytical techniques for the identification of unknown metabolites can be used to save valuable time and resources. (Graph Presented).

Iaith wreiddiolSaesneg
Tudalennau (o-i)3300-3309
Nifer y tudalennau10
CyfnodolynAnalytical Chemistry
Cyfrol89
Rhif cyhoeddi6
Dyddiad ar-lein cynnar27 Chwef 2017
Dynodwyr Gwrthrych Digidol (DOIs)
StatwsCyhoeddwyd - 21 Maw 2017

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