A hierarchical dynamic model used for investigating feed efficiency and its relationship with hepatic gene expression in APOE*3-Leiden.CETP mice

Background: Feed efficiency (FE) is an important trait for livestock and humans. While the livestock industry focuses on increasing FE, in the current obesogenic society it is more of interest to decrease FE. Hence, understanding mechanisms involved in the regulation of FE and particularly how it can be decreased would help tremendously in counteracting the obesity pandemic. However, it is difficult to accurately measure or calculate FE in humans. In this study, we aimed to address this challenge by developing a hierarchical dynamic model based on humanized mouse data. Methods: We analyzed existing experimental data derived from 105 APOE*3-Leiden.CETP (E3L.CETP) mice fed a high-fat high-cholesterol (HFHC) diet for 1 (N = 20), 2 (N = 19), 3 (N = 20), and 6 (N = 46) month. We developed an ordinary differential equation (ODE) based model to estimate the FE based on the longitudinal data of body weight and food intake. Since the liver plays an important role in maintaining metabolic homeostasis, we evaluated associations between FE and hepatic gene expression levels. Depending on the feeding duration, we observed different relationships between FE and hepatic gene expression levels. Results: After 1-month feeding of HFHC diet, we observed that FE was associated with vitamin A metabolism, arachidonic acid metabolism, and the PPAR signaling pathway. After 3- and 6-month feeding of HFHC diet, we observed that FE was associated most strongly with expression levels of Spink1 and H19, genes involved in cell proliferation and glucose metabolism, respectively. Conclusions: In conclusion, our analysis suggests that various biological processes such as vitamin A metabolism, hepatic response to inflammation, and cell proliferation associate with FE at different stages of diet-induced obesity.</p

Male apoE*3-Leiden.CETP mice on high-fat high-cholesterol diet exhibit a biphasic dyslipidemic response, mimicking the changes in plasma lipids observed through life in men

Physiological adaptations resulting in the development of the metabolic syndrome in man occur over a time span of several decades. This combined with the prohibitive financial cost and ethical concerns to measure key metabolic parameters repeatedly in subjects for the major part of their life span makes that comprehensive longitudinal human data sets are virtually nonexistent. While experimental mice are often used, little is known whether this species is in fact an adequate model to better understand the mechanisms that drive the metabolic syndrome in man. We took up the challenge to study the response of male apoE*3-Leiden.CETP mice (with a humanized lipid profile) to a high-fat high-cholesterol diet for 6 months. Study parameters include body weight, food intake, plasma and liver lipids, hepatic transcriptome, VLDL - triglyceride production and importantly the use of stable isotopes to measure hepatic de novo lipogenesis, gluconeogenesis, and biliary/fecal sterol secretion to assess metabolic fluxes. The key observations include (1) high inter-individual variation; (2) a largely unaffected hepatic transcriptome at 2, 3, and 6 months; (3) a biphasic response curve of the main metabolic features over time; and (4) maximum insulin resistance preceding dyslipidemia. The biphasic response in plasma triglyceride and total cholesterol appears to mimic that of men in cross-sectional studies. Combined, these observations suggest that studies such as these can help to delineate the causes of metabolic derangements in patients suffering from metabolic syndrome

Evaluating computational models of cholesterol metabolism

Regulation of cholesterol homeostasis has been studied extensively during the last decades. Many of the metabolic pathways involved have been discovered. Yet important gaps in our knowledge remain. For example, knowledge on intracellular cholesterol traffic and its relation to the regulation of cholesterol synthesis and plasma cholesterol levels is incomplete. One way of addressing the remaining questions is by making use of computational models. Here, we critically evaluate existing computational models of cholesterol metabolism making use of ordinary differential equations and addressed whether they used assumptions and make predictions in line with current knowledge on cholesterol homeostasis. Having studied the results described by the authors, we have also tested their models. This was done primarily by testing the effect of statin treatment in each model. Ten out of eleven models tested have made assumptions in line with current knowledge of cholesterol metabolism. Three out of the ten remaining models made correct predictions, i.e. predicting a decrease in plasma total and LDL cholesterol or increased uptake of LDL upon treatment upon the use of statins. In conclusion, few models on cholesterol metabolism are able to pass a functional test. Apparently most models have not undergone the critical iterative systems biology cycle of validation. We expect modeling of cholesterol metabolism to go through many more model topologies and iterative cycles and welcome the increased understanding of cholesterol metabolism these are likely to bring. (C) 2015 Elsevier B.V. All rights reserved

Developments in intestinal cholesterol transport and triglyceride absorption

Purpose of review To discuss recent advances in research focused on intestinal lipid handling. Recent findings An important strategy in reducing atherosclerosis and risk of cardiovascular events is to increase the rate of reverse cholesterol transport, including its final step; cholesterol excretion from the body. The rate of removal is determined by a complex interplay between the factors involved in regulation of intestinal cholesterol absorption. One of these factors is a process known as transintestinal cholesterol excretion. This pathway comprises transport of cholesterol directly from the blood, through the enterocyte, into the intestinal lumen. In humans, this pathway accounts for 35% of cholesterol excretion in the feces. Mechanistic studies in mice revealed that, activation of the bile acid receptor farnesoid X receptor increases cholesterol removal via the transintestinal cholesterol excretion pathway as well as decreases plasma cholesterol and triglyceride providing an interesting target for treatment of dyslipidemia in humans. The physical chemical properties of bile acids are under control of farnesoid X receptor and determine intestinal cholesterol and triglyceride solubilization as well as absorption, providing a direct link between these two important factors in the pathogenesis of cardiovascular disease. Besides bile acids, intestinal phospholipids are important for luminal lipid solubilization. Interestingly, phospholipid remodeling through LPCAT3 was shown to be pivotal for uptake of fatty acids by enterocytes, which may provide a mechanistic handle for therapeutic intervention. Summary The importance of the intestine in control of cholesterol and triglyceride homeostasis is increasingly recognized. Recently, novel factors involved in regulation of cholesterol excretion and intestinal triglyceride and fatty acid uptake have been reported and are discussed in this short review

A computational model for prediction of ferritin and haemoglobin levels in blood donors

Blood donors are at risk of iron deficiency anaemia. While this risk is decreased through ferritin-based deferral, ideally ferritin monitoring should also aid in optimising donation frequencies. We extended an existing model of haemoglobin (Hb) synthesis with iron homeostasis and validated the model on a cohort of 300 new donors whose ferritin levels were measured from stored blood samples collected over a 2-year period. We then used the donor's gender, body weight, height, and baseline Hb and ferritin levels to predict subsequent Hb and ferritin levels. The prediction error was within measurement variability in 88% of Hb level predictions and 64% of ferritin level predictions. A sensitivity analysis of the model revealed that baseline ferritin level was the most important in predicting future ferritin levels. Finally, we used the model to calculate the annual donation frequency at which donors would keep their ferritin level >15 ng/ml when measured after donating for 2 years. The mean annual donation frequency would then be 1.9 for women and 4.1 for men. The computational model, requiring baseline values only, can predict future Hb and ferritin levels remarkably well. This enables determination of optimal donation frequencies for individual donors at the start of their donation career

Journal des candidats au surnumérariat de l'enregistrement et des contributions directes

01 septembre 19291929/09/01 (A36,N3)

Outline of the NRP2Path matching process.

<p>The input for NRP2Path consists of mass shift sequences (or amino acid search tags) on the one hand, and genome sequences on the other hand. The latter are processed into databases by <i>makedb</i>, using antiSMASH and NRPSPredictor2. When a database is queried with a mass shift sequence or amino acid search tag, Pep2Path scores all possible matches between search tags and all possible assembly line configurations of each of the NRPS BGCs in the database.</p

Benchmark of Pep2Path on 18 recently discovered NRPS BGCs.

<p>For each tag size, all possible search tags of that size in the test set of peptides (<b><a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003822#pcbi.1003822.s003" target="_blank">Table S1</a></b>) were used as queries. For each BGC search space size, 50 search spaces were generated from randomly selected BGCs from the same (sub)phylum that the NRP originates from. The resulting percentages represent the average number of cases in which the correct BGC ended up as the (shared) best hit across all possible sequence tags and across all possible search space permutations. Shared best hits were included because of the frequent presence of orthologous BGCs encoding the same molecule in related genomes. The <i>n</i> in the left column signifies the number of test peptides large enough to be included in the analysis for this tag size; from each of these test peptides, all possible subtags were used in cases where the length of the tag is shorter than the length of the peptide.</p><p>Benchmark of Pep2Path on 18 recently discovered NRPS BGCs.</p

Novel matches of NORINE-derived NRPs to BGCs detected in genome sequences.

<p>Candidate BGCs for trichotoxin, ferintoic acid, plusbacin and amphibactin B were discovered by searching within the taxonomic range of the species in which the molecules were found. The candidate BGC for tripropeptin A was discovered by searching the entire Pep2Path database.</p><p>Novel matches of NORINE-derived NRPs to BGCs detected in genome sequences.</p

Quality of NRP2Path predictions with varying sequence tag lengths and NRPSPredictor2 prediction qualities.

<p>The heat map shows the average number of correct BGC predictions for Pep2Path searches with the stendomycin sequence tag V-V-T(S)-T(S)-A-I(L)-V-G across the <i>Streptomyces hygroscopicus</i> ATCC 53653 genome (20 NRPS BGCs) or across all <i>Streptomyces</i> nucleotide entries (342 NRPS BGCs). The searches were done for all possible search subtags of 2–8 amino acids long, and for all combinations of 0–8 simulated mispredictions for the corresponding NRPS modules. Mispredictions are simulated with zero scores given by Pep2Path for sequence tags matching to these domains.</p