Mathematical modelling of competitive LDL/VLDL binding and uptake by hepatocytes

The ethanol extract of Edgeworthia gardneri (Wall.) Meisn attenuates macrophage foam cell formation and atherogenesis in ApoE-/- mice

Introduction

Atherosclerotic cardiovascular disease is the leading cause of death worldwide. The Edgeworthia gardneri (Wall.) Meisn is a Tibetan medicine commonly used to prepare herbal tea to alleviate the local people's metabolic diseases. However, the anti-atherosclerotic effect of ethanol extract of the flower of E. gardneri (Wall.) Meisn (EEEG) and its underlying mechanism remain unknown.

Methods

EEEG was used to treat low-density lipoprotein (ox-LDL)-induced macrophages to detect macrophage foaminess, cholesterol binding and uptake, and lipid transport-related gene expression. eEEG treated ApoE^-/- mice fed a high-fat diet for 16 weeks to detect atherosclerotic plaque area, macrophage infiltration, and liver and small intestine lipid transport-related gene expression.

Results

EEEG inhibited macrophage-derived foam cell formation induced by oxidized low-density lipoprotein (ox-LDL) by reducing CD36-mediated lipoprotein uptake. EEEG significantly alleviated atherosclerosis in ApoE^-/- mice fed a high-fat diet for 16 weeks. EEEG treatment significantly decreased atherosclerotic plaque area, macrophage infiltration, and increased collagen content. Moreover, EEEG treatment significantly downregulated mRNA expression of hepatic Srb1 and intestinal Npc1l1 and increased expression of hepatic Cyp7a1.

Conclusion

Our study highlighted that EEEG played a role in attenuating atherosclerotic plaque formation by reducing macrophage foam cell formation.

Modeling cholesterol metabolism and atherosclerosis

Atherosclerotic cardiovascular disease (ASCVD) is the leading cause of morbidity and mortality among Western populations. Many risk factors have been identified for ASCVD; however, elevated low-density lipoprotein cholesterol (LDL-C) remains the gold standard. Cholesterol metabolism at the cellular and whole-body level is maintained by an array of interacting components. These regulatory mechanisms have complex behavior. Likewise, the mechanisms which underpin atherogenesis are nontrivial and multifaceted. To help overcome the challenge of investigating these processes mathematical modeling, which is a core constituent of the systems biology paradigm has played a pivotal role in deciphering their dynamics. In so doing models have revealed new insights about the key drivers of ASCVD. The aim of this review is fourfold; to provide an overview of cholesterol metabolism and atherosclerosis, to briefly introduce mathematical approaches used in this field, to critically discuss models of cholesterol metabolism and atherosclerosis, and to highlight areas where mathematical modeling could help to investigate in the future. This article is categorized under: Cardiovascular Diseases > Computational Models.

Inhibition of microRNA-128-3p attenuates hypercholesterolemia in mouse model

AIMS

Hypercholesterolemia remains a critical risk factor for cardiovascular diseases and there is an urgent need to develop effective alternative therapeutics. Herein, we investigated the effects of miR-128-3p inhibition on serum cholesterol levels using a hypercholesterolemic mouse model.

MATERIALS AND METHODS

Five injections of anti-miR-128-3p (AM-128) treatment were given, and the cholesterol profile in serum and liver was quantified. We validated the underlying gene network using qRT-PCR, western blotting, ELISA, and dual luciferase assays.

KEY FINDINGS

AM-128 treatment inhibits cholesterol biosynthesis by upregulating INSIG1 and downregulating HMGCR (3-hydroxy-3-methylglutaryl-CoA reductase) expression. The serum cholesterol clearance by SR-B1 (scavenger receptor class B member 1) and LDLR (low density lipoprotein receptors) was also increased. Furthermore, the catabolism of cholesterol by CYP7A1 (cytochrome P450 family 7 subfamily A member 1) was increased.

SIGNIFICANCE

Our results confirmed a critical role of miR-128-3p inhibition in lowering serum cholesterol and suggest its potential therapeutic implications in reversing hypercholesterolemia.

A mathematical model of the human metabolic system and metabolic flexibility

In healthy subjects some tissues in the human body display metabolic flexibility, by this we mean the ability for the tissue to switch its fuel source between predominantly carbohydrates in the postprandial state and predominantly fats in the fasted state. Many of the pathways involved with human metabolism are controlled by insulin and insulin-resistant states such as obesity and type-2 diabetes are characterised by a loss or impairment of metabolic flexibility. In this paper we derive a system of 12 first-order coupled differential equations that describe the transport between and storage in different tissues of the human body. We find steady state solutions to these equations and use these results to nondimensionalise the model. We then solve the model numerically to simulate a healthy balanced meal and a high fat meal and we discuss and compare these results. Our numerical results show good agreement with experimental data where we have data available to us and the results show behaviour that agrees with intuition where we currently have no data with which to compare.

Control of cholesterol homeostasis by entero-hepatic bile transport – the role of feedback mechanisms

Cholesterol homeostasis is achieved through a tight regulation between synthesis, dietary absorption, utilization of bile salts, and excretion in the entero-hepatic compartment.

Levels of high-density lipoprotein cholesterol (HDL-C) among children with steady-state sickle cell disease

BACKGROUND

The search for sickle cell disease (SCD) prognosis biomarkers is a challenge. These markers identification can help to establish further therapy, later severe clinical complications and with patients follow-up. We attempted to study a possible involvement of levels of high-density lipoprotein cholesterol (HDL-C) in steady-state children with SCD, once that this lipid marker has been correlated with anti-inflammatory, anti-oxidative, anti-aggregation, anti-coagulant and pro-fibrinolytic activities, important aspects to be considered in sickle cell disease pathogenesis.

METHODS

We prospectively analyzed biochemical, inflammatory and hematological biomarkers of 152 steady-state infants with SCD and 132 healthy subjects using immunochemistry, immunoassay and electronic cell counter respectively. Clinical data were collected from patient medical records.

RESULTS

Of the 152 infants investigated had a significant positive association of high-density lipoprotein cholesterol with hemoglobin (P < 0.001), hematocrit (P < 0.001) and total cholesterol (P < 0.001) and a negative significant association with reticulocytes (P = 0.046), leukocytes (P = 0.015), monocytes (P = 0.004) and platelets (P = 0.005), bilirubins [total bilirubin (P < 0.001), direct bilirubin (P < 0.001) and indirect bilirubin (P < 0.001], iron (P < 0.001), aminotransferases [aspartate aminotransferase (P = 0.004), alanine aminotransferase (P = 0.035)], lactate dehydrogenase (P < 0.001), urea (P = 0.030), alpha 1-antitrypsin (P < 0.001), very low-density lipoprotein cholesterol (P = 0.003), triglycerides (P = 0.005) and hemoglobin S (P = 0.002). Low high-density lipoprotein cholesterol concentration was associated with the history of cardiac abnormalities (P = 0.025), pneumonia (P = 0.033) and blood transfusion use (P = 0.025). Lipids and inflammatory markers were associated with the presence of cholelithiasis.

CONCLUSIONS

We hypothesize that some SCD patients can have a specific dyslipidemic subphenotype characterized by low HDL-C with hypertriglyceridemia and high VLDL-C in association with other biomarkers, including those related to inflammation. This represents an important step toward a more reliable clinical prognosis. Additional studies are warranted to test this hypothesis and the probably mechanisms involved in this complex network of markers and their role in SCD pathogenesis.

Statistical methods for pathway analysis of genome-wide data for association with complex genetic traits

A number of statistical methods have been developed to test for associations between pathways (collections of genes related biologically) and complex genetic traits. Pathway analysis methods were originally developed for analyzing gene expression data, but recently methods have been developed to perform pathway analysis on genome-wide association study (GWAS) data. The purpose of this review is to give an overview of these methods, enabling the reader to gain an understanding of what pathway analysis involves, and to select the method most suited to their purposes. This review describes the various types of statistical methods for pathway analysis, detailing the strengths and weaknesses of each. Factors influencing the power of pathway analyses, such as gene coverage and choice of pathways to analyze, are discussed, as well as various unresolved statistical issues. Finally, a list of computer programs for performing pathway analysis on genome-wide association data is provided.

Nutritional systems biology modeling: from molecular mechanisms to physiology

The use of computational modeling and simulation has increased in many biological fields, but despite their potential these techniques are only marginally applied in nutritional sciences. Nevertheless, recent applications of modeling have been instrumental in answering important nutritional questions from the cellular up to the physiological levels. Capturing the complexity of today's important nutritional research questions poses a challenge for modeling to become truly integrative in the consideration and interpretation of experimental data at widely differing scales of space and time. In this review, we discuss a selection of available modeling approaches and applications relevant for nutrition. We then put these models into perspective by categorizing them according to their space and time domain. Through this categorization process, we identified a dearth of models that consider processes occurring between the microscopic and macroscopic scale. We propose a "middle-out" strategy to develop the required full-scale, multilevel computational models. Exhaustive and accurate phenotyping, the use of the virtual patient concept, and the development of biomarkers from "-omics" signatures are identified as key elements of a successful systems biology modeling approach in nutrition research--one that integrates physiological mechanisms and data at multiple space and time scales.

Mathematical model for low density lipoprotein (LDL) endocytosis by hepatocytes

Individuals with elevated levels of plasma low density lipoprotein (LDL) cholesterol (LDL-C) are considered to be at risk of developing coronary heart disease. LDL particles are removed from the blood by a process known as receptor-mediated endocytosis, which occurs mainly in the liver. A series of classical experiments delineated the major steps in the endocytotic process; apolipoprotein B-100 present on LDL particles binds to a specific receptor (LDL receptor, LDL-R) in specialized areas of the cell surface called clathrin-coated pits. The pit comprising the LDL-LDL-R complex is internalized forming a cytoplasmic endosome. Fusion of the endosome with a lysosome leads to degradation of the LDL into its constituent parts (that is, cholesterol, fatty acids, and amino acids), which are released for reuse by the cell, or are excreted. In this paper, we formulate a mathematical model of LDL endocytosis, consisting of a system of ordinary differential equations. We validate our model against existing in vitro experimental data, and we use it to explore differences in system behavior when a single bolus of extracellular LDL is supplied to cells, compared to when a continuous supply of LDL particles is available. Whereas the former situation is common to in vitro experimental systems, the latter better reflects the in vivo situation. We use asymptotic analysis and numerical simulations to study the longtime behavior of model solutions. The implications of model-derived insights for experimental design are discussed.