Modeling Longitudinal Metabonomics and Microbiota Interactions in C57BL/6 Mice Fed a High Fat Diet

• Published in: Analytical chemistry (Washington) Vol. 88; no. 15; pp. 7617 - 7626
• Main Authors: Montoliu, Ivan, Cominetti, Ornella, Boulangé, Claire L., Berger, Bernard, Siddharth, Jay, Nicholson, Jeremy, Martin, François-Pierre J.
• Format: Journal Article
• Language: English
• Published: United States American Chemical Society 02.08.2016
• Subjects: Animals; Bacteria - genetics; Bacteria - isolation & purification; Body Weight; Diet, High-Fat; Feces - microbiology; Least-Squares Analysis; Longitudinal Studies; Magnetic Resonance Spectroscopy; Male; Metabolome; Metabolomics; Mice; Mice, Inbred C57BL; Microbiota; Principal Component Analysis; RNA, Ribosomal, 16S - chemistry; RNA, Ribosomal, 16S - genetics; RNA, Ribosomal, 16S - metabolism; Sequence Analysis, DNA; Urinalysis
• ISSN: 0003-2700; 1520-6882
• DOI: 10.1021/acs.analchem.6b01343

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.