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PLoS Comput Biol. 2014 May 1;10(5):e1003579. doi: 10.1371/journal.pcbi.1003579. eCollection 2014 May.

A computational model for the analysis of lipoprotein distributions in the mouse: translating FPLC profiles to lipoprotein metabolism.

Author information

1
Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands; Netherlands Consortium for Systems Biology, University of Amsterdam, Amsterdam, The Netherlands.
2
Department of Pediatrics, University Groningen, University Medical Center Groningen, Groningen, The Netherlands.
3
Netherlands Consortium for Systems Biology, University of Amsterdam, Amsterdam, The Netherlands; Department of Pediatrics, University Groningen, University Medical Center Groningen, Groningen, The Netherlands; Department of Laboratory Medicine, University Groningen, University Medical Center Groningen, Groningen, The Netherlands.

Abstract

Disturbances of lipoprotein metabolism are recognized as indicators of cardiometabolic disease risk. Lipoprotein size and composition, measured in a lipoprotein profile, are considered to be disease risk markers. However, the measured profile is a collective result of complex metabolic interactions, which complicates the identification of changes in metabolism. In this study we aim to develop a method which quantitatively relates murine lipoprotein size, composition and concentration to the molecular mechanisms underlying lipoprotein metabolism. We introduce a computational framework which incorporates a novel kinetic model of murine lipoprotein metabolism. The model is applied to compute a distribution of plasma lipoproteins, which is then related to experimental lipoprotein profiles through the generation of an in silico lipoprotein profile. The model was first applied to profiles obtained from wild-type C57Bl/6J mice. The results provided insight into the interplay of lipoprotein production, remodelling and catabolism. Moreover, the concentration and metabolism of unmeasured lipoprotein components could be determined. The model was validated through the prediction of lipoprotein profiles of several transgenic mouse models commonly used in cardiovascular research. Finally, the framework was employed for longitudinal analysis of the profiles of C57Bl/6J mice following a pharmaceutical intervention with a liver X receptor (LXR) agonist. The multifaceted regulatory response to the administration of the compound is incompletely understood. The results explain the characteristic changes of the observed lipoprotein profile in terms of the underlying metabolic perturbation and resultant modifications of lipid fluxes in the body. The Murine Lipoprotein Profiler (MuLiP) presented here is thus a valuable tool to assess the metabolic origin of altered murine lipoprotein profiles and can be applied in preclinical research performed in mice for analysis of lipid fluxes and lipoprotein composition.

PMID:
24784354
PMCID:
PMC4006703
DOI:
10.1371/journal.pcbi.1003579
[Indexed for MEDLINE]
Free PMC Article

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