Atherosclerosis and liver inflammation induced by increased dietary cholesterol intake: a combined transcriptomics and metabolomics analysis

With increasing dietary cholesterol intake the liver switches from a mainly resilient to a predominantly inflammatory state, which is associated with early lesion formation

Semi-automated non-target processing in GC × GC–MS metabolomics analysis: applicability for biomedical studies

Due to the complexity of typical metabolomics samples and the many steps required to obtain quantitative data in GC × GC–MS consisting of deconvolution, peak picking, peak merging, and integration, the unbiased non-target quantification of GC × GC–MS data still poses a major challenge in metabolomics analysis. The feasibility of using commercially available software for non-target processing of GC × GC–MS data was assessed. For this purpose a set of mouse liver samples (24 study samples and five quality control (QC) samples prepared from the study samples) were measured with GC × GC–MS and GC–MS to study the development and progression of insulin resistance, a primary characteristic of diabetes type 2. A total of 170 and 691 peaks were quantified in, respectively, the GC–MS and GC × GC–MS data for all study and QC samples. The quantitative results for the QC samples were compared to assess the quality of semi-automated GC × GC–MS processing compared to targeted GC–MS processing which involved time-consuming manual correction of all wrongly integrated metabolites and was considered as golden standard. The relative standard deviations (RSDs) obtained with GC × GC–MS were somewhat higher than with GC–MS, due to less accurate processing. Still, the biological information in the study samples was preserved and the added value of GC × GC–MS was demonstrated; many additional candidate biomarkers were found with GC × GC–MS compared to GC–MS

Insight in modulation of inflammation in response to diclofenac intervention: a human intervention study

Background. Chronic systemic low-grade inflammation in obese subjects is associated with health complications including cardiovascular diseases, insulin resistance and diabetes. Reducing inflammatory responses may reduce these risks. However, available markers of inflammatory status inadequately describe the complexity of metabolic responses to mild anti-inflammatory therapy. Methods. To address this limitation, we used an integrative omics approach to characterize modulation of inflammation in overweight men during an intervention with the non-steroidal anti-inflammatory drug diclofenac. Measured parameters included 80 plasma proteins, >300 plasma metabolites (lipids, free fatty acids, oxylipids and polar compounds) and an array of peripheral blood mononuclear cells (PBMC) gene expression products. These measures were submitted to multivariate and correlation analysis and were used for construction of biological response networks. Results. A panel of genes, proteins and metabolites, including PGE2 and TNF-alpha, were identified that describe a diclofenac-response network (68 genes in PBMC, 1 plasma protein and 4 plasma metabolites). Novel candidate markers of inflammatory modulation included PBMC expression of annexin A1 and caspase 8, and the arachidonic acid metabolite 5,6-DHET. Conclusion. In this study the integrated analysis of a wide range of parameters allowed the development of a network of markers responding to inflammatory modulation, thereby providing insight into the complex process of inflammation and ways to assess changes in inflammatory status associated with obesity. Trial registration. The study is registered as NCT00221052 in clinicaltrials.gov database. © 2010 van Erk et al; licensee BioMed Central Ltd

Plasma and Liver Lipidomics Response to an Intervention of Rimonabant in ApoE*3Leiden.CETP Transgenic Mice

Background: Lipids are known to play crucial roles in the development of life-style related risk factors such as obesity, dyslipoproteinemia, hypertension and diabetes. The first selective cannabinoid-1 receptor blocker rimonabant, an anorectic anti-obesity drug, was frequently used in conjunction with diet and exercise for patients with a body mass index greater than 30 kg/m2 with associated risk factors such as type II diabetes and dyslipidaemia in the past. Less is known about the impact of this drug on the regulation of lipid metabolism in plasma and liver in the early stage of obesity. Methodology/Principal Findings: We designed a four-week parallel controlled intervention on apolipoprotein E3 Leiden cholesteryl ester transfer protein (ApoE&z.ast;3Leiden.CETP) transgenic mice with mild overweight and hypercholesterolemia. A liquid chromatography-linear ion trap-Fourier transform ion cyclotron resonance-mass spectrometric approach was employed to investigate plasma and liver lipid responses to the rimonabant intervention. Rimonabant was found to induce a significant body weight loss (9.4%, p<0.05) and a significant plasma total cholesterol reduction (24%, p<0.05). Six plasma and three liver lipids in ApoE&z.ast;3Leiden.CETP transgenic mice were detected to most significantly respond to rimonabant treatment. Distinct lipid patterns between the mice were observed for both plasma and liver samples in rimonabant treatment vs. non-treated controls. This study successfully applied, for the first time, systems biology based lipidomics approaches to evaluate treatment effects of rimonabant in the early stage of obesity. Conclusion: The effects of rimonabant on lipid metabolism and body weight reduction in the early stage obesity were shown to be moderate in ApoE&z.ast;3Leiden.CETP mice on high-fat diet. © 2011 Hu et al

Lipidomics Reveals Multiple Pathway Effects of a Multi-Components Preparation on Lipid Biochemistry in ApoE*3Leiden.CETP Mice

Background: Causes and consequences of the complex changes in lipids occurring in the metabolic syndrome are only partly understood. Several interconnected processes are deteriorating, which implies that multi-target approaches might be more successful than strategies based on a limited number of surrogate markers. Preparations from Chinese Medicine (CM) systems have been handed down with documented clinical features similar as metabolic syndrome, which might help developing new intervention for metabolic syndrome. The progress in systems biology and specific animal models created possibilities to assess the effects of such preparations. Here we report the plasma and liver lipidomics results of the intervention effects of a preparation SUB885C in apolipoprotein E3 Leiden cholesteryl ester transfer protein (ApoE*3Leiden.CETP) mice. SUB885C was developed according to the principles of CM for treatment of metabolic syndrome. The cannabinoid receptor type 1 blocker rimonabant was included as a general control for the evaluation of weight and metabolic responses. Methodology/Principal Findings: ApoE*3Leiden.CETP mice with mild hypercholesterolemia were divided into SUB885C-, rimonabant- and non-treated control groups. SUB885C caused no weight loss, but significantly reduced plasma cholesterol (-49%, p <0.001), CETP levels (-31%,

Mass-spectrometry-based metabolomics: limitations and recommendations for future progress with particular focus on nutrition research

Mass spectrometry (MS) techniques, because of their sensitivity and selectivity, have become methods of choice to characterize the human metabolome and MS-based metabolomics is increasingly used to characterize the complex metabolic effects of nutrients or foods. However progress is still hampered by many unsolved problems and most notably the lack of well established and standardized methods or procedures, and the difficulties still met in the identification of the metabolites influenced by a given nutritional intervention. The purpose of this paper is to review the main obstacles limiting progress and to make recommendations to overcome them. Propositions are made to improve the mode of collection and preparation of biological samples, the coverage and quality of mass spectrometry analyses, the extraction and exploitation of the raw data, the identification of the metabolites and the biological interpretation of the results

The Relation between Non-adipose Muscle Fat and Hepatic Steatosis Studied with Localized 1H Magnetic Resonance Spectroscopy (1H MRS) and LC-MS Techniques

Aim/objective: In this study we investigated ectopic fat storage in the muscle and the liver using 1H Magnetic Resonance Spectroscopy (1H-MRS). The inability to store fat in adipose tissue leads to ectopic Triacylglycerol (TG) accumulation in muscle followed by the liver: the so called “overflow hypothesis”. It is assumed that when steatosis occurs in organs like the liver we can speak from “Metabolic Syndrome”. Methods: We compared the effects of two different diet interventions, 24 h-starvation and 40 days High-fat diet (+0.25% cholesterol and 45% energy from bovine lard) with control mice. Characterization of lipid molecular species in non-adipose muscle homogenate was performed by comparing the groups using liquid chromatography coupled to mass spectrometry (LC-MS) techniques following a Systems Biology lipidomics based approach. Reversed phase liquid chromatography coupled to mass spectrometry (LC-MS) were used to quantify and qualify the rearrangement and repartitioning of the triacylglycerol compound in the liver organ. Results: The major message of this manuscript is the interaction of remnant organ/tissue called “carcass” in the absorption capacity of lipids and the spill-over of these lipid compounds (mainly TG’s) to the liver. Our data suggest that if the remnant muscle compartment is saturated with lipids until ≈500 g/kg dry matter there is no TGs accumulation in the liver, but above this level there is a spill over in the plasma resulting in fat accumulation in the liver. Conclusion: We demonstrated in this study that fat can be stored in the muscle but when this compartment is saturated the liver takes over the function as a fat sink, the "overflow hypothesis" resulting finally in hepatic steatosis and ‘Metabolic Syndrome’

The comparison of lipid profiling in mouse brain and liver after starvation and a high-fat diet : A medical systems biology approach

We investigated with LC-MS techniques, measuring approximately 109 lipid compounds, in mouse brain and liver tissue after 48 hours of starvation and a High-Fat Diet if brain and liver lipid composition changed. We measured Cholesterolesters (ChE), Lysophosphatidyl-cholines (LPC), Phosphatidylcholine (PC), Sphingomyelin (SPM) and Triacylglycerols (TG's) for liver tissue while for brain tissue we had an extra lipid compound the Plasmalogens. In addition, dynamics of hepatic steatosis were determined in an in vivo mouse model with localized non-invasive Magnetic Resonance Spectroscopy (1H-MRS) techniques. In the experimental design Male C57bl6 mice (age 8-12 weeks) were exposed to three treatments: A: They were fed a chow Diet for a period of approximately 40 days (Control group); B: They were fed a High-Fat Diet, containing 0.25% cholesterol (Ch) and 24% energy from bovine lard for a period of approximately 40 days, C: Or they were exposed to 48 hours of starvation. For whole brain tissue of these mice groups the LC-MS techniques indicated that the brain was rather invulnerable to Dietary intervention. The (phospho-) lipid-composition of the brain was unchanged in the starvation group but the cholesterol-ester content was significantly increased in the high High-Fat Diet group. These observations suggest that the brain lipid composition is insensitive to starvation but can be affected by a high High-Fat Diet. In contrast, for liver tissue both 24 h starvation and the 40 day High-Fat Diet resulted in exponential hepatic fat accumulation, although their time course (measured with 1H MRS) techniques was distinctly different. Mass spectrometry (LC-MS) demonstrated for liver tissue remarkable differences in lipid profiles between treatments. 1H-MRS proved to be a reliable method for frequent, repetitive determination of hepatic fat in vivo and a noninvasive alternative to biopsy. Moreover, LC-MS and Principal Component Analysis (PCA) demonstrated that in liver tissue different lipid end products are formed as result of Dietary composition Apparently, for liver tissue starvation and a High-Fat Diet result in a process called hepatic steatosis which is regulated under both conditions via different metabolic pathways. In addition, 1H-MRS techniques demonstrated for liver that the relative amount of unsaturated bindings is significantly higher in the High-Fat Diet group (P≤0.001), which can be deducted from the relative intensities of the (CH=CH) elements and their conjugated unsaturated elements (C-CCH2C=C). We conclude, comparing brain vs. liver tissue that both tissues have a totally different metabolic response to both treatments. The brain is insensitive to starvation but can be affected by a High-Fat Diet while in liver tissue both treatments result paradoxically in a hepatic steatosis. However, for the liver, the dynamics and the lipid profiles of this process of this hepatic steatosis under starvation or a High-Fat Diet are totally different.</p

Assessing the performance of statistical validation tools for megavariate metabolomics data

Statistical model validation tools such as cross-validation, jack-knifing model parameters and permutation tests are meant to obtain an objective assessment of the performance and stability of a statistical model. However, little is known about the performance of these tools for megavariate data sets, having, for instance, a number of variables larger than 10 times the number of subjects. The performance is assessed for megavariate metabolomics data, but the conclusions also carry over to proteomics, transcriptomics and many other research areas. Partial least squares discriminant analyses models were built for several LC-MS lipidomic training data sets of various numbers of lean and obese subjects. The training data sets were compared on their modelling performance and their predictability using a 10-fold cross-validation, a permutation test, and test data sets. A wide range of cross-validation error rates was found (from 7.5% to 16.3% for the largest trainings set and from 0% to 60% for the smallest training set) and the error rate increased when the number of subjects decreased. The test error rates varied from 5% to 50%. The smaller the number of subjects compared to the number of variables, the less the outcome of validation tools such as cross-validation, jack-knifing model parameters and permutation tests can be trusted. The result depends crucially on the specific sample of subjects that is used for modelling. The validation tools cannot be used as warning mechanism for problems due to sample size or to representativity of the samplin

Building Multivariate Systems Biology Models

Systems biology methods using large-scale “omics” data sets face unique challenges: integrating and analyzing near limitless data space, while recognizing and removing systematic variation or noise. Herein we propose a complementary multivariate analysis workflow to both integrate “omics” data from disparate sources and analyze the results for specific and unique sample correlations. This workflow combines principal component analysis (PCA), orthogonal projections to latent structures discriminate analysis (OPLS-DA), orthogonal 2 projections to latent structures (O2PLS), and shared and unique structures (SUS) plots. The workflow is demonstrated using data from a study in which ApoE3Leiden mice were fed an atherogenic diet consisting of increasing cholesterol levels followed by therapeutic intervention (fenofibrate, rosuvastatin, and LXR activator T-0901317). The levels of structural lipids (lipidomics) and free fatty acids in liver were quantified via liquid chromatography–mass spectrometry (LC–MS). The complementary workflow identified diglycerides as key hepatic metabolites affected by dietary cholesterol and drug intervention. Modeling of the three therapeutics for mice fed a high-cholesterol diet further highlighted diglycerides as metabolites of interest in atherogenesis, suggesting a role in eliciting chronic liver inflammation. In particular, O2PLS-based SUS2 plots showed that treatment with T-0901317 or rosuvastatin returned the diglyceride profile in high-cholesterol-fed mice to that of control animals