Bayesian estimation of the number of protonation sites for urinary metabolites from NMR spectroscopic data
Author(s)
Ye, Lifeng
De Iorio, Maria
Ebbels, Timothy MD
Type
Journal Article
Abstract
Introduction
To aid the development of better algorithms for 1
H NMR data analysis, such as alignment or peak-fitting, it is important to characterise and model chemical shift changes caused by variation in pH. The number of protonation sites, a key parameter in the theoretical relationship between pH and chemical shift, is traditionally estimated from the molecular structure, which is often unknown in untargeted metabolomics applications.
Objective
We aim to use observed NMR chemical shift titration data to estimate the number of protonation sites for a range of urinary metabolites.
Methods
A pool of urine from healthy subjects was titrated in the range pH 2–12, standard 1
H NMR spectra were acquired and positions of 51 peaks (corresponding to 32 identified metabolites) were recorded. A theoretical model of chemical shift was fit to the data using a Bayesian statistical framework, using model selection procedures in a Markov Chain Monte Carlo algorithm to estimate the number of protonation sites for each molecule.
Results
The estimated number of protonation sites was found to be correct for 41 out of 51 peaks. In some cases, the number of sites was incorrectly estimated, due to very close pKa values or a limited amount of data in the required pH range.
Conclusions
Given appropriate data, it is possible to estimate the number of protonation sites for many metabolites typically observed in 1
H NMR metabolomics without knowledge of the molecular structure. This approach may be a valuable resource for the development of future automated metabolite alignment, annotation and peak fitting algorithms.
To aid the development of better algorithms for 1
H NMR data analysis, such as alignment or peak-fitting, it is important to characterise and model chemical shift changes caused by variation in pH. The number of protonation sites, a key parameter in the theoretical relationship between pH and chemical shift, is traditionally estimated from the molecular structure, which is often unknown in untargeted metabolomics applications.
Objective
We aim to use observed NMR chemical shift titration data to estimate the number of protonation sites for a range of urinary metabolites.
Methods
A pool of urine from healthy subjects was titrated in the range pH 2–12, standard 1
H NMR spectra were acquired and positions of 51 peaks (corresponding to 32 identified metabolites) were recorded. A theoretical model of chemical shift was fit to the data using a Bayesian statistical framework, using model selection procedures in a Markov Chain Monte Carlo algorithm to estimate the number of protonation sites for each molecule.
Results
The estimated number of protonation sites was found to be correct for 41 out of 51 peaks. In some cases, the number of sites was incorrectly estimated, due to very close pKa values or a limited amount of data in the required pH range.
Conclusions
Given appropriate data, it is possible to estimate the number of protonation sites for many metabolites typically observed in 1
H NMR metabolomics without knowledge of the molecular structure. This approach may be a valuable resource for the development of future automated metabolite alignment, annotation and peak fitting algorithms.
Date Issued
2018-05-01
Date Acceptance
2018-03-16
Citation
Metabolomics, 2018, 14 (5)
ISSN
1573-3882
Publisher
Springer Verlag
Journal / Book Title
Metabolomics
Volume
14
Issue
5
Copyright Statement
© The Author(s) 2018. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Identifier
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000431957900004&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
Subjects
Science & Technology
Life Sciences & Biomedicine
Endocrinology & Metabolism
NMR
pH
Peak shift changes
Protonation site
Bayesian model selection
ERROR
HMDB
Publication Status
Published
Article Number
ARTN 56
Date Publish Online
2018-03-26