Imperial College London

Emeritus ProfessorJeremyNicholson

Faculty of MedicineDepartment of Metabolism, Digestion and Reproduction

Emeritus Professor of Biological Chemistry
 
 
 
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Contact

 

+44 (0)20 7594 3195j.nicholson Website

 
 
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Assistant

 

Ms Wendy Torto +44 (0)20 7594 3225

 
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Location

 

Office no. 665Sir Alexander Fleming BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Montoliu:2016:10.1021/acs.analchem.6b01343,
author = {Montoliu, I and Cominetti, O and Boulangé, CL and Berger, B and Siddharth, J and Nicholson, J and Martin, FPJ},
doi = {10.1021/acs.analchem.6b01343},
journal = {Analytical Chemistry},
pages = {7617--7626},
title = {Modeling Longitudinal Metabonomics and Microbiota Interactions in C57BL/6 Mice Fed a High Fat Diet},
url = {http://dx.doi.org/10.1021/acs.analchem.6b01343},
volume = {88},
year = {2016}
}

RIS format (EndNote, RefMan)

TY  - JOUR
AB - Longitudinal studies aim typically at following populations of subjects over time and are important to understand the global evolution of biological processes. When it comes to longitudinal omics data, it will often depend on the overall objective of the study, and constraints imposed by the data, to define the appropriate modeling tools. Here, we report the use of multilevel simultaneous component analysis (MSCA), orthogonal projection on latent structures (OPLS), and regularized canonical correlation analysis (rCCA) to study associations between specific longitudinal urine metabonomics data and microbiome data in a diet-induced obesity model using C57BL/6 mice. 1H NMR urine metabolic profiling was performed on samples collected weekly over a period of 13 weeks, and stool microbial composition was assessed using 16S rRNA gene sequencing at three specific time periods (baseline, first week response, end of study). MSCA and OPLS allowed us to explore longitudinal urine metabonomics data in relation to the dietary groups, as well as dietary effects on body weight. In addition, we report a data integration strategy based on regularized CCA and correlation analyses of urine metabonomics data and 16S rRNA gene sequencing data to investigate the functional relationships between metabolites and gut microbial composition. Thanks to this workflow enabling the breakdown of this data set complexity, the most relevant patterns could be extracted to further explore physiological processes at an anthropometric, cellular, and molecular level.
AU - Montoliu,I
AU - Cominetti,O
AU - Boulangé,CL
AU - Berger,B
AU - Siddharth,J
AU - Nicholson,J
AU - Martin,FPJ
DO - 10.1021/acs.analchem.6b01343
EP - 7626
PY - 2016///
SN - 0003-2700
SP - 7617
TI - Modeling Longitudinal Metabonomics and Microbiota Interactions in C57BL/6 Mice Fed a High Fat Diet
T2 - Analytical Chemistry
UR - http://dx.doi.org/10.1021/acs.analchem.6b01343
VL - 88
ER -