A Computational Model for the Analysis of Lipoprotein Distributions in the Mouse: Translating FPLC Profiles to Lipoprotein Metabolism

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.

Trimethylamine-N-oxide (TMAO)-induced atherosclerosis is associated with bile acid metabolism

“…Serum total cholesterol (TC), triacylglycerol (TG) were measured using enzymatic reagent kits (Biosino, Beijing, China), while high-density lipoprotein cholesterol (HDL-c) was detected by phosphotungstic acid (PTA)-Mg 2+ precipitation method (Biosino, Beijing, China) and low-density lipoprotein cholesterol (LDL-c) was detected by polyvingel sulfate (PVS) sedimentation method (Biosino, Beijing, China). Plasma lipoproteins were fractionated by fast-protein liquid chromatography (FPLC) with slight modification according to Sips et al [14]. Briefly, serum (100 μL) was subjected to gel-filtration chromatography using Superdex 200 10/300 GL (GE Healthcare Bio-Sciences AB, Uppsala, Sweden).…”

The Protective Activities of Dietary Sea Cucumber Cerebrosides against Atherosclerosis through Regulating Inflammation and Cholesterol Metabolism in Male Mice

“…Plasma lipoproteins were fractionated by fast‐protein liquid chromatography (FPLC). Serum (150 uL) was subjected to gel‐filtration chromatography using Superdex 200 10/300 GL (GE Healthcare Bio‐Sciences AB, Uppsala, Sweden) as described . The system was run with a constant flow of 0.3 mL min −1 .…”

Mathematically modelling the dynamics of cholesterol metabolism and ageing

“…In recent years however, several groups have adopted a systems level approach to investigating lipid metabolism Mooney et al, 2016). The majority of these models have centred on specific metabolic processes, including those directed at lipoprotein metabolism (Hubner et al, 2008;Shorten and Upreti, 2005;Sips et al, 2014), cholesterol biosynthesis (Bhattacharya et al, 2014;Kervizic and Corcos, 2008;Mazein et al, 2013;Watterson et al, 2013), reverse cholesterol transport (Lu et al, 2014), adipocyte metabolism (Micheloni et al, 2015), hepatocyte metabolism (Jerby et al, 2010), cholesterol regulatory enzymes (Chapman et al, 2010), whole-body plasma cholesterol metabolism (van de Pas et al, 2012) and enterohepatic circulation of bile acids (Mishra et al, 2014). These models all have noteworthy features and have added to our understanding of lipid metabolism.…”