Application of a Bayesian deconvolution approach for high-resolution (1)H NMR spectra to assessing the metabolic effects of acute phenobarbital exposure in liver tissue

High-resolution (1)H NMR spectroscopy is frequently used in the field of metabolomics to assess the metabolites found in biofluids or tissue extracts to define a metabolic profile that describes a given biological process. In this study, we aimed to increase the utility of NMR-based metabolomics by...

Full description

Saved in:
Bibliographic Details
Published in:Analytical chemistry (Washington) 2010-06, Vol.82 (11), p.4479
Main Authors: Rubtsov, Denis V, Waterman, Claire, Currie, Richard A, Waterfield, Catherine, Salazar, José Domingo, Wright, Jayne, Griffin, Julian L
Format: Article
Language:English
Subjects:
Animals
Bayes Theorem
Biomarkers - metabolism
False Positive Reactions
Glycine - metabolism
Liver - cytology
Liver - drug effects
Liver - metabolism
Magnetic Resonance Spectroscopy - statistics & numerical data
Male
Metabolomics - methods
Multivariate Analysis
Phenobarbital - toxicity
Rats
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:High-resolution (1)H NMR spectroscopy is frequently used in the field of metabolomics to assess the metabolites found in biofluids or tissue extracts to define a metabolic profile that describes a given biological process. In this study, we aimed to increase the utility of NMR-based metabolomics by using advanced Bayesian modeling of the time-domain high-resolution 1D NMR free induction decay (FID). The improvement over traditional nonparametric binning is twofold and associated with enhanced resolution of the analysis and automation of the signal processing stage. The automation is achieved by using a Bayesian formalism for all parameters of the model including the number of components. The approach is illustrated with a study of early markers of acute exposure to different doses of a well-characterized nongenotoxic hepatocarcinogen, phenobarbital, in rats. The results demonstrate that Bayesian deconvolution produces a better model for the NMR spectra that allows the identification of subtle changes in metabolic concentrations and a decrease in the expected false discovery rate compared with approaches based on "binning". These properties suggest that Bayesian deconvolution could facilitate the biomarker discovery process and improve information extraction from high-resolution NMR spectra.
ISSN:1520-6882
DOI:10.1021/ac100344m