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

Modifier variant of METTL13 suppresses human GAB1-associated profound deafness

A modifier variant can abrogate the risk of a monogenic disorder. DFNM1 is a locus on chromosome 1 encoding a dominant suppressor of human DFNB26 recessive, profound deafness. Here, we report that DFNB26 is associated with a substitution (p.Gly116Glu) in the pleckstrin homology domain of GRB2-associated binding protein 1 (GAB1), an essential scaffold in the MET proto-oncogene, receptor tyrosine kinase/HGF (MET/HGF) pathway. A dominant substitution (p.Arg544Gln) of METTL13, encoding a predicted methyltransferase, is the DFNM1 suppressor of GAB1-associated deafness. In zebrafish, human METTL13 mRNA harboring the modifier allele rescued the GAB1-associated morphant phenotype. In mice, GAB1 and METTL13 colocalized in auditory sensory neurons, and METTL13 coimmunoprecipitated with GAB1 and SPRY2, indicating at least a tripartite complex. Expression of MET-signaling genes in human lymphoblastoid cells of individuals homozygous for p.Gly116Glu GAB1 revealed dysregulation of HGF, MET, SHP2, and SPRY2, all of which have reported variants associated with deafness. However, SPRY2 was not dysregulated in normal-hearing humans homozygous for both the GAB1 DFNB26 deafness variant and the dominant METTL13 deafness suppressor, indicating a plausible mechanism of suppression. Identification of METTL13-based modification of MET signaling offers a potential therapeutic strategy for a wide range of associated hearing disorders. Furthermore, MET signaling is essential for diverse functions in many tissues including the inner ear. Therefore, identification of the modifier of MET signaling is likely to have broad clinical implications.

Computational Modeling of Lipid Metabolism in Yeast

Lipid metabolism is essential for all major cell functions and has recently gained increasing attention in research and health studies. However, mathematical modeling by means of classical approaches such as stoichiometric networks and ordinary differential equation systems has not yet provided satisfactory insights, due to the complexity of lipid metabolism characterized by many different species with only slight differences and by promiscuous multifunctional enzymes. Here, we present an object-oriented stochastic model approach as a way to cope with the complex lipid metabolic network. While all lipid species are treated objects in the model, they can be modified by the respective converting reactions based on reaction rules, a hybrid method that integrates benefits of agent-based and classical stochastic simulation. This approach allows to follow the dynamics of all lipid species with different fatty acids, different degrees of saturation and different headgroups over time and to analyze the effect of parameter changes, potential mutations in the catalyzing enzymes or provision of different precursors. Applied to yeast metabolism during one cell cycle period, we could analyze the distribution of all lipids to the various membranes in time-dependent manner. The presented approach allows to efficiently treat the complexity of cellular lipid metabolism and to derive conclusions on the time- and location-dependent distributions of lipid species and their properties such as saturation. It is widely applicable, easily extendable and will provide further insights in healthy and diseased states of cell metabolism.

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

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 (56.3 µM/h), β-LCAT (15.62 µM/h), and cholesteryl ester (CE) transfer protein-mediated flux of CE from HDL to IDL/VLDL (21.5 µM/h) based on data from 15 healthy individuals. In a second step, we estimated LDL's HL activity (3.19 pools/day) and, for the very first time, selective CE efflux from LDL (8.39 µM/h) by relying on the previously derived reaction rates. The estimated metabolic rates were then confirmed in an independent group (n = 10). Although measurement uncertainties do not permit us to estimate parameters in individuals, the novel approach we describe here offers the unique possibility to investigate lipoprotein dynamics in various diseases like atherosclerosis or diabetes.

Zonation of hepatic fatty acid metabolism - The diversity of its regulation and the benefit of modeling

A pronounced heterogeneity between hepatocytes in subcellular structure and enzyme activities was discovered more than 50years ago and initiated the idea of metabolic zonation. In the last decades zonation patterns of liver metabolism were extensively investigated for carbohydrate, nitrogen and lipid metabolism. The present review focuses on zonation patterns of the latter. We review recent findings regarding the zonation of fatty acid uptake and oxidation, ketogenesis, triglyceride synthesis and secretion, de novo lipogenesis, as well as bile acid and cholesterol metabolism. In doing so, we expose knowledge gaps and discuss contradictory experimental results, for example on the zonation pattern of fatty acid oxidation and de novo lipogenesis. Thus, possible rewarding directions of further research are identified. Furthermore, recent findings about the regulation of metabolic zonation are summarized, especially regarding the role of hormones, nerve innervation, morphogens, gender differences and the influence of the circadian clock. In the last part of the review, a short collection of models considering hepatic lipid metabolism is provided. We conclude that modeling, despite its proven benefit for understanding of hepatic carbohydrate and ammonia metabolisms, has so far been largely disregarded in the study of lipid metabolism; therefore some possible fields of modeling interest are presented.

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

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.

An in-silico model of lipoprotein metabolism and kinetics for the evaluation of targets and biomarkers in the reverse cholesterol transport pathway

High-density lipoprotein (HDL) is believed to play an important role in lowering cardiovascular disease (CVD) risk by mediating the process of reverse cholesterol transport (RCT). Via RCT, excess cholesterol from peripheral tissues is carried back to the liver and hence should lead to the reduction of atherosclerotic plaques. The recent failures of HDL-cholesterol (HDL-C) raising therapies have initiated a re-examination of the link between CVD risk and the rate of RCT, and have brought into question whether all target modulations that raise HDL-C would be atheroprotective. To help address these issues, a novel in-silico model has been built to incorporate modern concepts of HDL biology, including: the geometric structure of HDL linking the core radius with the number of ApoA-I molecules on it, and the regeneration of lipid-poor ApoA-I from spherical HDL due to remodeling processes. The ODE model has been calibrated using data from the literature and validated by simulating additional experiments not used in the calibration. Using a virtual population, we show that the model provides possible explanations for a number of well-known relationships in cholesterol metabolism, including the epidemiological relationship between HDL-C and CVD risk and the correlations between some HDL-related lipoprotein markers. In particular, the model has been used to explore two HDL-C raising target modulations, Cholesteryl Ester Transfer Protein (CETP) inhibition and ATP-binding cassette transporter member 1 (ABCA1) up-regulation. It predicts that while CETP inhibition would not result in an increased RCT rate, ABCA1 up-regulation should increase both HDL-C and RCT rate. Furthermore, the model predicts the two target modulations result in distinct changes in the lipoprotein measures. Finally, the model also allows for an evaluation of two candidate biomarkers for in-vivo whole-body ABCA1 activity: the absolute concentration and the % lipid-poor ApoA-I. These findings illustrate the potential utility of the model in drug development.

A mathematical model of the sterol regulatory element binding protein 2 cholesterol biosynthesis pathway

Cholesterol is one of the key constituents for maintaining the cellular membrane and thus the integrity of the cell itself. In contrast high levels of cholesterol in the blood are known to be a major risk factor in the development of cardiovascular disease. We formulate a deterministic nonlinear ordinary differential equation model of the sterol regulatory element binding protein 2 (SREBP-2) cholesterol genetic regulatory pathway in a hepatocyte. The mathematical model includes a description of genetic transcription by SREBP-2 which is subsequently translated to mRNA leading to the formation of 3-hydroxy-3-methylglutaryl coenzyme A reductase (HMGCR), a main regulator of cholesterol synthesis. Cholesterol synthesis subsequently leads to the regulation of SREBP-2 via a negative feedback formulation. Parameterised with data from the literature, the model is used to understand how SREBP-2 transcription and regulation affects cellular cholesterol concentration. Model stability analysis shows that the only positive steady-state of the system exhibits purely oscillatory, damped oscillatory or monotic behaviour under certain parameter conditions. In light of our findings we postulate how cholesterol homeostasis is maintained within the cell and the advantages of our model formulation are discussed with respect to other models of genetic regulation within the literature.

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We formulate and analyse a nonlinear ODE model of the SREBP2 pathway.

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The mathematical model exhibits stable limit cycles under certain parameter conditions.

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Negative feedbacks in the SREBP2 pathway may help regulate cholesterol homeostasis.

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Our model provides a more accurate formulation of genetic regulation using nonlinear ODEs.

A whole-body mathematical model of cholesterol metabolism and its age-associated dysregulation

BACKGROUND

Global demographic changes have stimulated marked interest in the process of aging. There has been, and will continue to be, an unrelenting rise in the number of the oldest old ( >85 years of age). Together with an ageing population there comes an increase in the prevalence of age related disease. Of the diseases of ageing, cardiovascular disease (CVD) has by far the highest prevalence. It is regarded that a finely tuned lipid profile may help to prevent CVD as there is a long established relationship between alterations to lipid metabolism and CVD risk. In fact elevated plasma cholesterol, particularly Low Density Lipoprotein Cholesterol (LDL-C) has consistently stood out as a risk factor for having a cardiovascular event. Moreover it is widely acknowledged that LDL-C may rise with age in both sexes in a wide variety of groups. The aim of this work was to use a whole-body mathematical model to investigate why LDL-C rises with age, and to test the hypothesis that mechanistic changes to cholesterol absorption and LDL-C removal from the plasma are responsible for the rise. The whole-body mechanistic nature of the model differs from previous models of cholesterol metabolism which have either focused on intracellular cholesterol homeostasis or have concentrated on an isolated area of lipoprotein dynamics. The model integrates both current and previously published data relating to molecular biology, physiology, ageing and nutrition in an integrated fashion.

RESULTS

The model was used to test the hypothesis that alterations to the rate of cholesterol absorption and changes to the rate of removal of LDL-C from the plasma are integral to understanding why LDL-C rises with age. The model demonstrates that increasing the rate of intestinal cholesterol absorption from 50% to 80% by age 65 years can result in an increase of LDL-C by as much as 34 mg/dL in a hypothetical male subject. The model also shows that decreasing the rate of hepatic clearance of LDL-C gradually to 50% by age 65 years can result in an increase of LDL-C by as much as 116 mg/dL.

CONCLUSIONS

Our model clearly demonstrates that of the two putative mechanisms that have been implicated in the dysregulation of cholesterol metabolism with age, alterations to the removal rate of plasma LDL-C has the most significant impact on cholesterol metabolism and small changes to the number of hepatic LDL receptors can result in a significant rise in LDL-C. This first whole-body systems based model of cholesterol balance could potentially be used as a tool to further improve our understanding of whole-body cholesterol metabolism and its dysregulation with age. Furthermore, given further fine tuning the model may help to investigate potential dietary and lifestyle regimes that have the potential to mitigate the effects aging has on cholesterol metabolism.

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

Increased plasma cholesterol concentration is associated with increased risk of cardiovascular disease. This study describes the development, validation, and analysis of a physiologically based kinetic (PBK) model for the prediction of plasma cholesterol concentrations in humans. This model was directly adapted from a PBK model for mice by incorporation of the reaction catalyzed by cholesterol ester transfer protein and contained 21 biochemical reactions and eight different cholesterol pools. The model was calibrated using published data for humans and validated by comparing model predictions on plasma cholesterol levels of subjects with 10 different genetic mutations (including familial hypercholesterolemia and Smith-Lemli-Opitz syndrome) with experimental data. Average model predictions on total cholesterol were accurate within 36% of the experimental data, which was within the experimental margin. Sensitivity analysis of the model indicated that the HDL cholesterol (HDL-C) concentration was mainly dependent on hepatic transport of cholesterol to HDL, cholesterol ester transfer from HDL to non-HDL, and hepatic uptake of cholesterol from non-HDL-C. Thus, the presented PBK model is a valid tool to predict the effect of genetic mutations on cholesterol concentrations, opening the way for future studies on the effect of different drugs on cholesterol levels in various subpopulations in silico.

A physiologically-based kinetic model for the prediction of plasma cholesterol concentrations in the mouse

The LDL cholesterol (LDL-C) and HDL cholesterol (HDL-C) concentrations are determined by the activity of a complex network of reactions in several organs. Physiologically-based kinetic (PBK) computational models can be used to describe these different reactions in an integrated, quantitative manner. A PBK model to predict plasma cholesterol levels in the mouse was developed, validated, and analyzed. Kinetic parameters required for defining the model were obtained using data from published experiments. To construct the model, a set of appropriate submodels was selected from a set of 65,536 submodels differing in the kinetic expressions of the reactions. A submodel was considered appropriate if it had the ability to correctly predict an increased or decreased plasma cholesterol level for a training set of 5 knockout mouse strains. The model thus defined consisted of 8 appropriate submodels and was validated using data from an independent set of 9 knockout mouse strains. The model prediction is the average prediction of 8 appropriate submodels. Remarkably, these submodels had in common that the rate of cholesterol transport from the liver to HDL was not dependent on hepatic cholesterol concentrations. The model appeared able to accurately predict in a quantitative way the plasma cholesterol concentrations of all 14 knockout strains considered, including the frequently used Ldlr-/- and Apoe-/- mouse strains. The model presented is a useful tool to predict the effect of knocking out genes that act in important steps in cholesterol metabolism on total plasma cholesterol, HDL-C and LDL-C in the mouse.

Genome scan of a nonword repetition phenotype in families with dyslexia: evidence for multiple loci

To understand the genetic architecture of dyslexia and identify the locations of genes involved, we performed linkage analyses in multigenerational families using a phonological memory phenotype--Nonword Repetition (NWR). A genome scan was first performed on 438 people from 51 families (DS-1) and linkage was assessed using variance components (VC), Bayesian oligogenic (BO), and parametric analyses. For replication, the genome scan and analyses were repeated on 693 people from 93 families (DS-2). For the combined set (DS-C), analyses were performed with all three methods in the regions that were identified in both samples. In DS-1, regions on chromosomes 4p, 6q, 12p, 17q, and 22q exceeded our initial threshold for linkage, with 17q providing a parametric LOD score of 3.2. Analysis with DS-2 confirmed the locations on chromosomes 4p and 12p. The strongest VC and BO signals in both samples were on chromosome 4p in DS-C, with a parametric multipoint LOD(max) of 2.36 for the 4p locus. Our linkage analyses of NWR in dyslexia provide suggestive and reproducible evidence for linkage to 4p12 and 12p in both samples, and significant evidence for linkage to 17q in one of the samples. These results warrant further studies of phonological memory and chromosomal regions identified here in other datasets.

Computational lipidology: predicting lipoprotein density profiles in human blood plasma

Monitoring cholesterol levels is strongly recommended to identify patients at risk for myocardial infarction. However, clinical markers beyond "bad" and "good" cholesterol are needed to precisely predict individual lipid disorders. Our work contributes to this aim by bringing together experiment and theory. We developed a novel computer-based model of the human plasma lipoprotein metabolism in order to simulate the blood lipid levels in high resolution. Instead of focusing on a few conventionally used predefined lipoprotein density classes (LDL, HDL), we consider the entire protein and lipid composition spectrum of individual lipoprotein complexes. Subsequently, their distribution over density (which equals the lipoprotein profile) is calculated. As our main results, we (i) successfully reproduced clinically measured lipoprotein profiles of healthy subjects; (ii) assigned lipoproteins to narrow density classes, named high-resolution density sub-fractions (hrDS), revealing heterogeneous lipoprotein distributions within the major lipoprotein classes; and (iii) present model-based predictions of changes in the lipoprotein distribution elicited by disorders in underlying molecular processes. In its present state, the model offers a platform for many future applications aimed at understanding the reasons for inter-individual variability, identifying new sub-fractions of potential clinical relevance and a patient-oriented diagnosis of the potential molecular causes for individual dyslipidemia.

A mathematical model of fatty acid metabolism and VLDL assembly in human liver

The lipid composition of very-low-density lipoprotein (VLDL) in plasma is crucial for human health. A pre-requisite for the alteration of VLDL composition is a co-ordinated understanding of the complex interactions in VLDL assembly. In order to determine the potential effects of changes in substrate availability on VLDL lipid composition, we constructed, parameterized and evaluated a mechanistic mathematical model of the biosynthesis of triglycerides, phospholipids, and cholesterol esters and the assembly of VLDL in human hepatocytes. Using published data on human liver metabolism, the model was also used to provide insight into the complex process of lipid metabolism and to estimate the affinities of different liver enzymes for different fatty acids (FA). For example, we found that Delta6-desaturase is 19 times more selective for C18:3n-3 than C18:2n-6, stearoyl-CoA-desaturase is 2.7 times more selective for C18:0 than C16:0, Delta5-desaturase desaturates C20:4n-3 preferentially over C20:3n-6 and FA elongase preferentially elongates C18:3n-6. The model was also used to predict the plasma free fatty acid (FFA) composition required to generate a prescribed change in plasma lipoprotein FA composition. Furthermore, the model was tested against a published human feeding trial that investigated the effect of changes in dietary FA composition on human plasma lipid FA composition. The model is a useful tool for predicting the effect of changes in plasma FFA composition on plasma lipoprotein lipid FA composition.

Comparative genome analysis and pathway reconstruction

Pathway reconstruction builds on genome and biochemical data with the aim of reconstructing higher level interactions between identified enzymes in a specific genome, in particular the different enzyme pathways (species or individual/patient). Metabolite flow in a pathway is analyzed by different tools, such as elementary mode analysis. This reveals key enzymes and pharmacological targets in the enzyme network. An overview of bioinformatic tools and algorithms for these tasks, application examples and recent results from these techniques are presented. Target selection, drug development and optimization can all be sped up using these approaches.

Gene-diet interaction and plasma lipid response to dietary intervention

Research in the field of gene-diet interactions as determinants of plasma lipid response to dietary interventions has accumulated a substantial body of evidence during the past decade. Several candidate genes have shown some promise as potential markers of individual dietary responsiveness. Among the best characterized are the APOE, APOA4, APOB, APOC3, and LPL loci. Other genes are being continuously incorporated to this most interesting search. However, in very few cases has consensus been achieved about the usefulness of genetic markers as clinically significant predictors of dietary response. The increased ability to generate genotypic information, in combination with the knowledge from the human genome project and more comprehensive experimental designs, will dramatically improve our capacity to answer many of our current questions. It will also help to prove that knowledge of an individual's genetic background will facilitate more precise dietary counseling and intervention, and more efficacious primary and secondary coronary heart disease prevention.