A pathway model of lipid metabolism to predict the effect of genetic variability on lipid levels

Knoblauch, Hans; Schuster, Herbert; Luft, Friedrich C.; Reich, Jens

doi:10.1007/s001090000156

Cited by 28 publications

(22 citation statements)

References 19 publications

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“…Using experimental data they obtained good estimates on the change in free, bound and internalised LDLR and on the rates of LDL binding and unbinding from the cell surface, bound particle-receptor complex internalisation and subsequent breakdown of the complex and recycling of the receptors. The work of Knoblauch et al (2000) and August et al (2007) has modelled the interactions between various lipoprotein particles (LDL, VLDL, intermediate density lipoprotein (IDL), high density lipoprotein (HDL)) and their endocytosis. Knoblauch et al (2000) used their model to show that decreasing LDLR causes an exponential increase in LDL levels, whilst other particle levels remain relatively constant.…”

Section: Introductionmentioning

confidence: 99%

“…The work of Knoblauch et al (2000) and August et al (2007) has modelled the interactions between various lipoprotein particles (LDL, VLDL, intermediate density lipoprotein (IDL), high density lipoprotein (HDL)) and their endocytosis. Knoblauch et al (2000) used their model to show that decreasing LDLR causes an exponential increase in LDL levels, whilst other particle levels remain relatively constant. August et al (2007) used their model of VLDL, IDL, LDL and the LDLR to calculate the various steady-states of the metabolic system and how perturbations from these affect the intracellular cholesterol concentration.…”

Section: Introductionmentioning

confidence: 99%

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A continuum receptor model of hepatic lipoprotein metabolism

Tindall¹,

Wattis

O’Malley

et al. 2009

Journal of Theoretical Biology

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A continuum receptor model of hepatic lipoprotein metabolism

Tindall¹,

Wattis

O’Malley

et al. 2009

Journal of Theoretical Biology

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“…In silico models have been used for various purposes in the study of cholesterol metabolism, such as in the interpretation of isotope-labeling studies (4)(5)(6)33 ), in the analysis of the regulatory pathway of cholesterol synthesis ( 34 ), or in making predictions of the effect of genetic mutations or food and drug interventions ( 35,36 ). These models, however, could predict the effect of a few genetic mutations only.…”

Section: Discussionmentioning

confidence: 99%

A physiologically based in silico kinetic model predicting plasma cholesterol concentrations in humans

Pas

Woutersen

Ommen

et al. 2012

Journal of Lipid Research

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This article is available online at http://www.jlr.org these cholesterol modeling studies present models that focus on LDL cholesterol (LDL-C) metabolism in plasma ( 4-6 ) or on cellular cholesterol metabolism ( 7 ). These models do not represent all relevant components of whole body cholesterol homeostasis because they lack reactions such as cholesterol absorption and biosynthesis in organs, which are the reactions targeted by important cholesterol-lowering drugs, such as statins. This implies that the models cannot fully explain how relevant plasma cholesterol-associated biomarkers are infl uenced by these drug interventions.We have, therefore, developed an in silico physiologically based kinetic (PBK) model for plasma cholesterol in the mouse that includes all relevant reactions and that correctly predicts the plasma cholesterol levels of a large variety of mouse strains with gene knockouts related to cholesterol metabolism ( 11 ). The model, of which the structure is given in Fig. 1 , is able to predict HDL cholesterol (HDL-C), non-HDL cholesterol (non-HDL-C), and total plasma cholesterol (TC) concentrations ( 12 ) as well as the intra-organ pools representing hepatic free cholesterol (Liv-FC), peripheral cholesterol (Per-C), intestinal cholesterol ester (Int-CE), hepatic cholesterol ester (Liv-CE), and intestinal free cholesterol (Int-FC) in the mouse. A similar model for humans will be of considerable value in predicting effects of drugs and genetic variations on plasma cholesterol concentrations. Therefore, the aim of this work is to adapt our model to a human version.Turnover of cholesterol in humans is quantitatively and qualitatively different from that in mice ( 13 ). Qualitative difference resides mainly in the protein cholesteryl ester In silico modeling has proven to be a useful tool in biology because it allows the study of interspecies variation and regulation of homeostasis and allows the integration of information from various sources ( 1-3 ). There are several modeling efforts on cholesterol ( 4-7 ), an important biomarker for the risk for cardiovascular events ( 8-10 ). Most of Manuscript received 4 September 2012 and in revised form 28 September 2012. Published, JLR Papers in Press, September 29, 2012 DOI 10.1194 A physiologically based in silico kinetic model predicting plasma cholesterol concentrations in humans Abbreviations: C, cholesterol (sum of FC and CE); CE, cholesterol ester; CETP, cholesterol ester transfer protein; FC, free cholesterol; FH, familial hypercholesterolemia; GWAS, genome-wide association study; PBK, physiologically based kinetic; SC, sensitivity coeffi cient; SLOS, Smith-Lemli-Opitz syndrome; TC, total plasma cholesterol (sum of HDL-C and non-HDL-C).

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“…These data are then evaluated using (multi-) compartmental modeling based on sets of differential equations (17)(18)(19)(20)(21). Besides this tracer kinetic approach, there are other less common approaches to model certain aspects of the dynamics of lipoprotein metabolism (22)(23)(24)(25).…”

Section: The Parameter Cetp Tgmentioning

confidence: 99%

In silico modeling of the dynamics of low density lipoprotein composition via a single plasma sample

Jansen

Pfaffelhuber

Hoffmann

et al. 2016

Journal of Lipid Research

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Due to the high variability of lipid and apolipoprotein composition, lipoproteins form a heterogeneous set of particles characterized by different features and metabolic fate. Lipoproteins are classified according to their density. In the healthy fasting state, established classes of lipoproteins are VLDLs, IDLs, LDLs, and HDLs (2), which can be further subdivided into lipoprotein subfractions.Not only the concentration of LDL cholesterol but also the particle size/density distribution in LDL is associated with CVDs. LDL can be differentiated into large-buoyant and small-dense LDL (lb-LDL and sd-LDL, respectively). LDL subtype patterns with predominantly sd-LDL (3) or with predominantly lb-LDL (4) are associated with an increased risk for CVDs.Although the clinical assessment of lipoprotein metabolism and risk prediction are based on simple measurements of steady-state concentrations like LDL-and HDL-cholesterol, lipoprotein metabolism is a dynamic transport process of lipids. In fact, most of the potential disease-causing effects of lipoproteins may lie yet unresolved in the transient nature of lipoprotein metabolism and lipid fluxes.LDL is primarily cleared from plasma via holoparticle uptake mediated by the LDL receptor, which requires apolipoprotein B-100 (ApoB) as a ligand. The exchange of TG and CE between lipoprotein particles is predominantly facilitated by the enzyme cholesteryl ester transfer protein (CETP). There are other enzymes and receptors besides CETP affecting LDL. Particles may lose TG and PL by the action of lipases. FC is converted to CE by the action of LCAT (5), and CE can be removed by selective CE efflux (6-9).Different enzymes hydrolyze TG in lipoproteins in plasma. The most important are LPL and HL. While LPL Abstract Lipoproteins play a key role in the development of CVD, but the dynamics of lipoprotein metabolism are difficult to address experimentally. This article describes a novel two-step combined in vitro and in silico approach that enables the estimation of key reactions in lipoprotein metabolism using just one blood sample. Lipoproteins were isolated by ultracentrifugation from fasting plasma stored at 4°C. Plasma incubated at 37°C is no longer in a steady state, and changes in composition may be determined. From these changes, we estimated rates for reactions like LCAT Human lipoprotein metabolism is highly relevant to the development of atherosclerosis and the understanding of CVD (1).The lipid-protein complex of lipoproteins consists of a core, made up of cholesteryl ester (CE), TGs, and a small proportion of free cholesterol (FC), whereas the shell consists of a phospholipid (PL) and FC monolayer. Furthermore, at least one apolipoprotein is attached to the particle.

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A pathway model of lipid metabolism to predict the effect of genetic variability on lipid levels

Cited by 28 publications

References 19 publications

A continuum receptor model of hepatic lipoprotein metabolism

A continuum receptor model of hepatic lipoprotein metabolism

A physiologically based in silico kinetic model predicting plasma cholesterol concentrations in humans

In silico modeling of the dynamics of low density lipoprotein composition via a single plasma sample

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