An integrated analytical and statistical two-dimensional spectroscopy strategy for metabolite identification: application to dietary biomarkers

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Title: An integrated analytical and statistical two-dimensional spectroscopy strategy for metabolite identification: application to dietary biomarkers
Authors: Posma, JM
Garcia Perez, I
Heaton, JC
Burdisso, P
Mathers, JC
Draper, J
Lewis, M
Lindon, JC
Frost, G
Holmes, E
Nicholson, JK
Item Type: Journal Article
Abstract: 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 datasets and show that application of complementary statistical and analytical techniques for the identification of unknown metabolites can be used to save valuable time and resource.
Editors: Larive, C
Issue Date: 27-Feb-2017
Date of Acceptance: 27-Feb-2017
URI: http://hdl.handle.net/10044/1/45084
DOI: https://dxx.doi.org/10.1021/acs.analchem.6b03324
ISSN: 1086-4377
Publisher: American Chemical Society
Start Page: 3300
End Page: 3309
Journal / Book Title: Analytical Chemistry
Volume: 89
Issue: 6
Copyright Statement: © 2017 American Chemical Society. This is an open access article published under a Creative Commons Attribution (CC-BY) License, which permits unrestricted use, distribution and reproduction in any medium, provided the author and source are cited.
Sponsor/Funder: Medical Research Council (MRC)
National Institute for Health Research
Medical Research Council (MRC)
National Institute for Health Research
Funder's Grant Number: MC_PC_12025
PDF-2012-05-456
10731
NF-SI-0513-10029
Keywords: Analytical Chemistry
0301 Analytical Chemistry
0904 Chemical Engineering
0399 Other Chemical Sciences
Publication Status: Published
Appears in Collections:Division of Surgery
Department of Medicine
Faculty of Medicine



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