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The majority of intestinal in vitro screening models use cell lines that do not reflect the complexity of the human intestinal tract and hence often fail to accurately predict intestinal drug absorption. Tissue explants have intact intestinal architecture and cell type diversity, but show short viability in static conditions. Here, we present a medium throughput microphysiological system, Intestinal Explant Barrier Chip (IEBC), that creates a dynamic microfluidic microenvironment and prolongs tissue viability. Using a snap fit mechanism, we successfully incorporated human and porcine colon tissue explants and studied tissue functionality, integrity and viability for 24 hours. With a proper distinction of transcellular over paracellular transport (ratio >2), tissue functionality was good at early and late timepoints. Low leakage of FITC-dextran and preserved intracellular lactate dehydrogenase levels indicate maintained tissue integrity and viability, respectively. From a selection of low to high permeability drugs, 6 out of 7 properly ranked according to their fraction absorbed. In conclusion, the IEBC is a novel screening platform benefitting from the complexity of tissue explants and the flow in microfluidic chips.

Research consortia in Europe often compete with each other for skills, human and technical resources and, eventually, recognition of the scientific impact of their work. In response to the same EU Horizon2020 call, we received funding for our research project proposals to identify and validate novel drug targets for cardiovascular disease treatment. Each consortium followed a unique and independent research strategy. However, as coordinators of these consortia we envisioned we could increase impact, outcomes and efficiency by intensifying our interaction. At an agreed stage during our projects we chose to share our knowledge, vision and ideas. In this paper we present what we learned, in the hope that future consortia will see the benefits of this approach.

Scope: Milk fat globule membrane (MFGM) is an essential component of milk. Bovine MFGM (bMFGM) has been shown to support cognitive development and increase relative concentrations of serum phospholipids. This study investigated bioavailability of bMFGM components after oral administration in two preclinical models to explore whether dietary bMFGM induced parallel changes to plasma and brain lipidomes. Methods and results: Transgenic APOE*3.Leiden mice (n = 18/group) and Sprague-Dawley rats (n = 12/group) were fed bMFGM-enriched (MFGM+) or Control diet, after which phospholipid profiles were determined in peripheral plasma, hippocampus and prefrontal cortex tissue by targeted mass spectrometry. Multivariate analysis of lipidomic profiles demonstrated a clear separation between MFGM+ and Control plasma samples across rodents. In plasma, sphingomyelins contributed the most to the separation of lipid patterns among both models, where three sphingomyelins (d18:1/14:0, d18:1/23:0, d18:1/23:1[9Z]) were significantly and consistently higher in the circulation of MFGM+ versus Control groups. A similar trend was observed in rat prefrontal cortex, although no significant separation of the brain lipidome was demonstrated. Conclusion: bMFGM-enriched diet alters plasma phospholipid composition in rodents, predominantly increasing sphingomyelin levels in the systemic circulation with some similar, but non-significant, trends in central regions of the brain. These changes may contribute to the beneficial effects of dietary bMFGM on neurodevelopment during early life.

The development of obesity is characterized by the metabolic overload of tissues and subsequent organ inflammation. The health effects of krill oil (KrO) on obesity-associated inflammation remain largely elusive, because long-term treatments with KrO have not been performed to date. Therefore, we examined the putative health effects of 28 weeks of 3% (w/w) KrO supplementation to an obesogenic diet (HFD) with fat derived mostly from lard. The HFD with KrO was compared to an HFD control group to evaluate the effects on fatty acid composition and associated inflammation in epididymal white adipose tissue (eWAT) and the liver during obesity development. KrO treatment increased the concentrations of EPA and DHA and associated oxylipins, including 18-HEPE, RvE2 and 14-HDHA in eWAT and the liver. Simultaneously, KrO decreased arachidonic acid concentrations and arachidonic-acid-derived oxylipins (e.g., HETEs, PGD2, PGE2, PGF2α, TXB2). In eWAT, KrO activated regulators of adipogenesis (e.g., PPARγ, CEBPα, KLF15, STAT5A), induced a shift towards smaller adipocytes and increased the total adipocyte numbers indicative for hyperplasia. KrO reduced crown-like structures in eWAT, and suppressed HFD-stimulated inflammatory pathways including TNFα and CCL2/MCP-1 signaling. The observed eWAT changes were accompanied by reduced plasma leptin and increased plasma adiponectin levels over time, and improved insulin resistance (HOMA-IR). In the liver, KrO suppressed inflammatory signaling pathways, including those controlled by IL-1β and M-CSF, without affecting liver histology. Furthermore, KrO deactivated hepatic REL-A/p65-NF-κB signaling, consistent with increased PPARα protein expression and a trend towards an increase in IkBα. In conclusion, long-term KrO treatment increased several anti-inflammatory PUFAs and oxylipins in WAT and the liver. These changes were accompanied by beneficial effects on general metabolism and inflammatory tone at the tissue level. The stimulation of adipogenesis by KrO allows for safe fat storage and may, together with more direct PPAR-mediated anti-inflammatory mechanisms, attenuate inflammation.

Background: Altered lipid metabolism in early life has been associated with subsequent weight gain and predicting this could aid in obesity prevention and risk management. Here, a lipidomic approach was used to identify circulating markers for future obesity risk in translational murine models and validate in a human infant cohort.Methods: Lipidomics was performed on the plasma of APOE*3 Leiden, Ldlr-/-.Leiden, and the wild-type C57BL/6J mice to capture candidate biomarkers predicting subsequent obesity parameters after exposure to high-fat diet. The identified candidate biomarkers were mapped onto corresponding lipid metabolism pathways and were investigated in the Cambridge Baby Growth Study. Infants' growth and adiposity were measured at 0-24 months. Capillary dried blood spots were sampled at 3 months for lipid profiling analysis.Findings: From the mouse models, cholesteryl esters were correlated with subsequent weight gain and other obesity parameters after HFD period (Spearman's r >= 0.5, FDR p values

Background & Aims The incidence of nonalcoholic steatohepatitis (NASH) is increasing. The pathophysiological mechanisms of NASH and the sequence of events leading to hepatic fibrosis are incompletely understood. The aim of this study was to gain insight into the dynamics of key molecular processes involved in NASH and to rank early markers for hepatic fibrosis. Methods A time-course study in low-density lipoprotein–receptor knockout. Leiden mice on a high-fat diet was performed to identify the temporal dynamics of key processes contributing to NASH and fibrosis. An integrative systems biology approach was used to elucidate candidate markers linked to the active fibrosis process by combining transcriptomics, dynamic proteomics, and histopathology. The translational value of these findings were confirmed using human NASH data sets. Results High-fat-diet feeding resulted in obesity, hyperlipidemia, insulin resistance, and NASH with fibrosis in a time-dependent manner. Temporal dynamics of key molecular processes involved in the development of NASH were identified, including lipid metabolism, inflammation, oxidative stress, and fibrosis. A data-integrative approach enabled identification of the active fibrotic process preceding histopathologic detection using a novel molecular fibrosis signature. Human studies were used to identify overlap of genes and processes and to perform a network biology-based prioritization to rank top candidate markers representing the early manifestation of fibrosis. Conclusions An early predictive molecular signature was identified that marked the active profibrotic process before histopathologic fibrosis becomes manifest. Early detection of the onset of NASH and fibrosis enables identification of novel blood-based biomarkers to stratify patients at risk, development of new therapeutics, and help shorten (pre)clinical experimental time frames., Graphical abstract

Background: The consumption of products rich in cereal fiber and with a low glycemic index is implicated in a lower risk of metabolic diseases. Previously, we showed that the consumption of fiber-rich pasta compared with bread resulted in a lower rate of appearance of exogenous glucose and a lower glucose clearance rate quantified with a dual-isotope technique, which was in accordance with a lower insulin and glucose-dependent insulinotropic polypeptide response.Objective: To gain more insight into the acute metabolic consequences of the consumption of products resulting in differential glucose kinetics, postprandial metabolic profiles were determined.Methods: In a crossover study, 9 healthy men (mean +/- SEM age: 21 +/- 0.5 y; mean SEM body mass index (kg/m(2)): 22 +/- 0.5] consumed wheat bread (132 g) and fresh pasta (119 g uncooked) enriched with wheat bran (10%) meals. A total of 134 different metabolites in postprandial plasma samples (at -5, 30, 60, 90, 120, and 180 min) were quantified by using a gas chromatography mass spectrometry based metabolomics approach (secondary outcomes). Two-factor ANOVA and advanced multivariate statistical analysis (partial least squares) were applied to detect differences between both food products.Results: Forty-two different postprandial metabolite profiles were identified, primarily representing pathways related to protein and energy metabolism, which were on average 8% and 7% lower after the men consumed pasta rather than bread, whereas concentrations of arabinose and xylose were 58% and 53% higher, respectively. Arabinose and xylose are derived from arabinoxylans, which are important components of wheat bran. The higher bioavailability of arabinose and xylose after pasta intake coincided with a lower rate of appearance of glucose and amino acids. We speculate that this higher bioavailability is due to higher degradation of arabinoxylans by small intestinal microbiota, facilitated by the higher viscosity of arabinoxylans after pasta intake than after bread intake.Conclusion: This study suggests that wheat bran, depending on the method of processing, can increase the viscosity of the meal bolus in the small intestine and interfere with macronutrient absorption in healthy men, thereby influencing postprandial glucose and insulin responses. This trial was registered at www.controlled-trials.com as ISRCTN42106325.

Obesity characterized by adiposity and ectopic fat accumulation is associated with the development of non-alcoholic fatty liver disease (NAFLD). Treatments that stimulate lipid utilization may prevent the development of obesity and comorbidities. This study evaluated the potential anti-obesogenic hepatoprotective effects of combined treatment with L-carnitine and nicotinamide riboside, i.e., components that can enhance fatty acid transfer across the inner mitochondrial membrane and increase nicotinamide adenine nucleotide (NAD+) levels, which are necessary for &beta, oxidation and the TCA cycle, respectively. Ldlr &minus, /&minus, Leiden mice were treated with high-fat diet (HFD) supplemented with L-carnitine (LC, 0.4% w/w), nicotinamide riboside (NR, 0.3% w/w) or both (COMBI) for 21 weeks. L-carnitine plasma levels were reduced by HFD and normalized by LC. NR supplementation raised its plasma metabolite levels demonstrating effective delivery. Although food intake and ambulatory activity were comparable in all groups, COMBI treatment significantly attenuated HFD-induced body weight gain, fat mass gain (&minus, 17%) and hepatic steatosis (&minus, 22%). Also, NR and COMBI reduced hepatic 4-hydroxynonenal adducts. Upstream-regulator gene analysis demonstrated that COMBI reversed detrimental effects of HFD on liver metabolism pathways and associated regulators, e.g., ACOX, SCAP, SREBF, PPARGC1B, and INSR. Combination treatment with LC and NR exerts protective effects on metabolic pathways and constitutes a new approach to attenuate HFD-induced obesity and NAFLD.

CONTEXT: Metabolic dysregulation underlies key metabolic risk factors—obesity, dyslipidemia, and dysglycemia. OBJECTIVE: To uncover mechanistic links between metabolomic dysregulation and metabolic risk by testing metabolite associations with risk factors cross-sectionally and with risk factor changes over time. DESIGN: Cross-sectional—discovery samples (n = 650; age, 36–69 years) from the Framingham Heart Study (FHS) and replication samples (n = 670; age, 61–76 years) from the BioImage Study, both following a factorial design sampled from high vs low strata of body mass index, lipids, and glucose. Longitudinal—FHS participants (n = 554) with 5–7 years of follow-up for risk factor changes. SETTING: Observational studies. PARTICIPANTS: Cross-sectional samples with or without obesity, dysglycemia, and dyslipidemia, excluding prevalent cardiovascular disease and diabetes or dyslipidemia treatment. Age- and sex-matched by group. INTERVENTIONS: None. MAIN OUTCOME MEASURE(S): Gas chromatography-mass spectrometry detected 119 plasma metabolites. Cross-sectional associations with obesity, dyslipidemia, and dysglycemia were tested in discovery, with external replication of 37 metabolites. Single- and multi-metabolite markers were tested for association with longitudinal changes in risk factors. RESULTS: Cross-sectional metabolite associations were identified with obesity (n = 26), dyslipidemia (n = 21), and dysglycemia (n = 11) in discovery. Glutamic acid, lactic acid, and sitosterol associated with all three risk factors in meta-analysis (P < 4.5 × 10−4). Metabolites associated with longitudinal risk factor changes were enriched for bioactive lipids. Multi-metabolite panels explained 2.5–15.3% of longitudinal changes in metabolic traits. CONCLUSIONS: Cross-sectional results implicated dysregulated glutamate cycling and amino acid metabolism in metabolic risk. Certain bioactive lipids were associated with risk factors cross-sectionally and over time, suggesting their upstream role in risk factor progression. Functional studies are needed to validate findings and facilitate translation into treatments or preventive measures. Fil: Yin, Xiaoyan. Framingham Heart Study; Estados Unidos. Boston University; Estados Unidos Fil: Subramanian, Subha. Framingham Heart Study; Estados Unidos. National Institutes of Health; Estados Unidos Fil: Willinger, Christine M.. Framingham Heart Study; Estados Unidos. National Institutes of Health; Estados Unidos Fil: Chen, George. Framingham Heart Study; Estados Unidos. National Institutes of Health; Estados Unidos Fil: Juhasz, Peter. BG Medicine; Estados Unidos Fil: Courchesne, Paul. Framingham Heart Study; Estados Unidos. National Institutes of Health; Estados Unidos Fil: Chen, Brian H.. Framingham Heart Study; Estados Unidos. National Institutes of Health; Estados Unidos Fil: Li, Xiaohang. BG Medicine; Estados Unidos Fil: Hwang, Shih Jen. Framingham Heart Study; Estados Unidos. National Institutes of Health; Estados Unidos Fil: Fox, Caroline S.. Framingham Heart Study; Estados Unidos. National Institutes of Health; Estados Unidos. Brigham and Women’s Hospital. Department of Medicine; Estados Unidos. Harvard Medical School; Estados Unidos Fil: O'Donnell, Christopher J.. Framingham Heart Study; Estados Unidos. National Institutes of Health; Estados Unidos. Massachusetts General Hospital. Department of Medicine; Estados Unidos. Harvard Medical School; Estados Unidos Fil: Muntendam, Pieter. BG Medicine; Estados Unidos Fil: Fuster, Valentin. Mt. Sinai School of Medicine; Estados Unidos. Centro Nacional de Investigaciones Cardiovasculares; España. Cedars Sinai Medical Center; Estados Unidos Fil: Bobeldijk Pastorova, Ivana. TNO Triskelion BV; Países Bajos Fil: Sookoian, Silvia Cristina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Investigaciones Médicas. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones Médicas; Argentina Fil: Pirola, Carlos José. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Investigaciones Médicas. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones Médicas; Argentina Fil: Gordon, Neal. BG Medicine; Estados Unidos Fil: Adourian, Aram. BG Medicine; Estados Unidos Fil: Larson, Martin G.. Framingham Heart Study; Estados Unidos. Boston University; Estados Unidos Fil: Levy, Daniel. Framingham Heart Study; Estados Unidos. National Institutes of Health; Estados Unidos. Boston University; Estados Unidos

We report a sensitive, generic method for quantitative profiling of bile acids and other endogenous metabolites in small quantities of various biological fluids and tissues. The method is based on a straightforward sample preparation, separation by reversed-phase high performance liquid-chromatography mass spectrometry (HPLC-MS) and electrospray ionisation in the negative ionisation mode (ESI-). Detection is performed in full scan using the linear ion trap Fourier transform mass spectrometer (LTQ-FTMS) generating data for many (endogenous) metabolites, not only bile acids. A validation of the method in urine, plasma and liver was performed for 17 bile acids including their taurine, sulfate and glycine conjugates. The method is linear in the 0.01-1 μM range. The accuracy in human plasma ranges from 74 to 113%, in human urine 77 to 104% and in mouse liver 79 to 140%. The precision ranges from 2 to 20% for pooled samples even in studies with large number of samples (n > 250). The method was successfully applied to a multi-compartmental APOE*3-Leiden mouse study, the main goal of which was to analyze the effect of increasing dietary cholesterol concentrations on hepatic cholesterol homeostasis and bile acid synthesis. Serum and liver samples from different treatment groups were profiled with the new method. Statistically significant differences between the diet groups were observed regarding total as well as individual bile acid concentrations. © 2008 Elsevier B.V. All rights reserved.

Differences in genetic background and/or environmental exposure among individuals are expected to give rise to differences in measurable characteristics, or phenotypes. Consequently, genetic resemblance and similarities in environment should manifest as similarities in phenotypes. The metabolome reflects many of the system properties, and is therefore an important part of the phenotype. Nevertheless, it has not yet been examined to what extent individuals sharing part of their genome and/or environment indeed have similar metabolomes. Here we present the results of hierarchical clustering of blood plasma lipid profile data obtained by liquid chromatographymass spectrometry from 23 healthy, 18-year-old twin pairs, of which 21 pairs were monozygotic, and 8 of their siblings. For 13 monozygotic twin pairs, within-pair similarities in relative concentrations of the detected lipids were indeed larger than the similarities with any other study participant. We demonstrate such high coclustering to be unexpected on basis of chance. The similarities between dizygotic twins and between nontwin siblings, as well as between nonfamilial participants, were less pronounced. In a number of twin pairs, within-pair dissimilarity of lipid profiles positively correlated with increased blood plasma concentrations of C-reactive protein in one twin. In conclusion, this study demonstrates that in healthy individuals, the individual genetic background contributes to the blood plasma lipid profile. Furthermore, lipid profiling may prove useful in monitoring health status, for example, in the context of personalized medicine. © 2008 Mary Ann Liebert, Inc. Chemicals / CAS: C-Reactive Protein, 9007-41-4; Lipids

Objective: Lipid profiling (lipidomics) may be useful in revealing detailed information with regard to the effects on lipid metabolism, the cardiovascular risk and to differentiate between therapies. The aims of the present study were to: (1) analyze in depth the lipid changes induced by rosuvastatin and atorvastatin at different dosages; (2) compare differences between the two drugs with respect to the lipid profile change; (3) relate the findings with meaningful pathological mechanisms of coronary artery disease. Research design and methods: Liquid chromatography-mass spectrometry was applied to obtain the metabolite profiles of plasma samples taken from a prospectively defined subset (n = 80) of participants in the RADAR study where a randomly assigned treatment with rosuvastatin or atorvastatin in increasing dosages was administered during an 18-week period. Results: A number of sphingomyelins (SPMs) and phosphatidylcholines (PGs) correlate with the different effects of the two statins on the LDL-C/HDL-C ratio. Rosuvastatin increased the plasma concentration of PCs after 6 and 18 weeks, while atorvastatin reduced the plasma concentrations of PCs at both timepoints and dosages (p

We introduce the metabolomics and proteomics based Postprandial Challenge Test (PCT) to quantify the postprandial response of multiple metabolic processes in humans in a standardized manner. The PCT comprised consumption of a standardized 500 ml dairy shake containing respectively 59, 30 and 12 energy percent lipids, carbohydrates and protein. During a 6 h time course after PCT 145 plasma metabolites, 79 proteins and 7 clinical chemistry parameters were quantified. Multiple processes related to metabolism, oxidation and inflammation reacted to the PCT, as demonstrated by changes of 106 metabolites, 31 proteins and 5 clinical chemistry parameters. The PCT was applied in a dietary intervention study to evaluate if the PCT would reveal additional metabolic changes compared to non-perturbed conditions. The study consisted of a 5-week intervention with a supplement mix of anti-inflammatory compounds in a crossover design with 36 overweight subjects. Of the 231 quantified parameters, 31 had different responses over time between treated and control groups, revealing differences in amino acid metabolism, oxidative stress, inflammation and endocrine metabolism. The results showed that the acute, short term metabolic responses to the PCT were different in subjects on the supplement mix compared to the controls. The PCT provided additional metabolic changes related to the dietary intervention not observed in non-perturbed conditions. Thus, a metabolomics based quantification of a standardized perturbation of metabolic homeostasis is more informative on metabolic status and subtle health effects induced by (dietary) interventions than quantification of the homeostatic situation. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11306-011-0320-5) contains supplementary material, which is available to authorized users.

BACKGROUND: The sequence of events leading to the development of insulin resistance (IR) as well as the underlying pathophysiological mechanisms are incompletely understood. As reductionist approaches have been largely unsuccessful in providing an understanding of the pathogenesis of IR, there is a need for an integrative, time-resolved approach to elucidate the development of the disease. METHODOLOGY/PRINCIPAL FINDINGS: Male ApoE3Leiden transgenic mice exhibiting a humanized lipid metabolism were fed a high-fat diet (HFD) for 0, 1, 6, 9, or 12 weeks. Development of IR was monitored in individual mice over time by performing glucose tolerance tests and measuring specific biomarkers in plasma, and hyperinsulinemic-euglycemic clamp analysis to assess IR in a tissue-specific manner. To elucidate the dynamics and tissue-specificity of metabolic and inflammatory processes key to IR development, a time-resolved systems analysis of gene expression and metabolite levels in liver, white adipose tissue (WAT), and muscle was performed. During HFD feeding, the mice became increasingly obese and showed a gradual increase in glucose intolerance. IR became first manifest in liver (week 6) and then in WAT (week 12), while skeletal muscle remained insulin-sensitive. Microarray analysis showed rapid upregulation of carbohydrate (only liver) and lipid metabolism genes (liver, WAT). Metabolomics revealed significant changes in the ratio of saturated to polyunsaturated fatty acids (liver, WAT, plasma) and in the concentrations of glucose, gluconeogenesis and Krebs cycle metabolites, and branched amino acids (liver). HFD evoked an early hepatic inflammatory response which then gradually declined to near baseline. By contrast, inflammation in WAT increased over time, reaching highest values in week 12. In skeletal muscle, carbohydrate metabolism, lipid metabolism, and inflammation was gradually suppressed with HFD. CONCLUSIONS/SIGNIFICANCE: HFD-induced IR is a time- and tissue-dependent process that starts in liver and proceeds in WAT. IR development is paralleled by tissue-specific gene expression changes, metabolic adjustments, changes in lipid composition, and inflammatory responses in liver and WAT involving p65-NFkB and SOCS3. The alterations in skeletal muscle are largely opposite to those in liver and WAT.

Combination of data sets from different objects (for example, from two groups of healthy volunteers from the same population) that were measured on a common set of variables (for example, metabolites or peptides) is desirable for statistical analysis in "omics" studies because it increases power. However, this type of combination is not directly possible if nonbiological systematic differences exist among the individual data sets, or "blocks". Such differences can, for example, be due to small analytical changes that are likely to accumulate over large time intervals between blocks of measurements. In this article we present a data transformation method, that we will refer to as "quantile equating", which per variable corrects for linear and nonlinear differences in distribution among blocks of semiquantitative data obtained with the same analytical method. We demonstrate the successful application of the quantile equating method to data obtained on two typical metabolomics platforms, i.e., liquid chromatography-mass spectrometry and nuclear magnetic resonance spectroscopy. We suggest uni- and multivariate methods to evaluate similarities and differences among data blocks before and after quantile equating. In conclusion, we have developed a method to correct for nonbiological systematic differences among semiquantitative data blocks and have demonstrated its successful application to metabolomics data sets. © 2010 American Chemical Society.

Analytical errors caused by suboptimal performance of the chosen platform for a number of metabolites and instrumental drift are a major issue in large-scale metabolomics studies. Especially for MS-based methods, which are gaining common ground within metabolomics, it is difficult to control the analytical data quality without the availability of suitable labeled internal standards and calibration standards even within one laboratory. In this paper, we suggest a workflow for significant reduction of the analytical error using pooled calibration samples and multiple internal standard strategy. Between and within batch calibration techniques are applied and the analytical error is reduced significantly (increase of 25% of peaks with RSD lower than 20%) and does not hamper or interfere with statistical analysis of the final data.

Background In the fields of life sciences, so-called designed studies are used for studying complex biological systems. The data derived from these studies comply with a study design aimed at generating relevant information while diminishing unwanted variation (noise). Knowledge about the study design can be used to decompose the total data into data blocks that are associated with specific effects. Subsequent statistical analysis can be improved by this decomposition if these are applied on selected combinations of effects. Results The benefit of this approach was demonstrated with an analysis that combines multivariate PLS (Partial Least Squares) regression with data decomposition from ANOVA (Analysis of Variance): ANOVA-PLS. As a case, a nutritional intervention study is used on Apoliprotein E3-Leiden (APOE3Leiden) transgenic mice to study the relation between liver lipidomics and a plasma inflammation marker, Serum Amyloid A. The ANOVA-PLS performance was compared to PLS regression on the non-decomposed data with respect to the quality of the modelled relation, model reliability, and interpretability. Conclusion It was shown that ANOVA-PLS leads to a better statistical model that is more reliable and better interpretable compared to standard PLS analysis. From a following biological interpretation, more relevant metabolites were derived from the model. The concept of combining data composition with a subsequent statistical analysis, as in ANOVA-PLS, is however not limited to PLS regression in metabolomics but can be applied for many statistical methods and many different types of data.

High-throughput biomolecular profiling techniques such as transcriptomics, proteomics and metabolomics are increasingly being used in in vivo studies to recognize and characterize effects of xenobiotics on organs and systems. Of particular interest are biomarkers of treatment-related effects which are detectible in easily accessible biological fluids such as blood. A fundamental challenge in such biomarker studies is selecting among the plethora of biomolecular changes induced by a compound and revealed by molecular profiling, to identify biomarkers which are exclusively or predominantly due to specific processes. In this work we present a cross-compartment correlation network approach, involving no a priori supervision or design, to integrate proteomic, metabolomic and transcriptomic data for selecting circulating biomarkers. The case study we present is the identification of biomarkers of drug-induced hepatic toxicity effects in a rodent model. Biomolecular profiling of both blood plasma and liver tissue from Wistar Hannover rats administered a toxic compound yielded many hundreds of statistically significant molecular changes. We exploited drug-induced correlations between blood plasma analytes and liver tissue molecules across study animals in order to nominate selected plasma molecules as biomarkers of drug-induced hepatic alterations of lipid metabolism and urea cycle processes. This journal is © The Royal Society of Chemistry.

A large metabolomics study was performed on 600 plasma samples taken at four time points before and after a single intake of a high fat test meal by obese and lean subjects. All samples were analyzed by a liquid chromatography-mass spectrometry (LC-MS) lipidomic method for metabolic profiling. A pragmatic approach combining several well-established statistical methods was developed for processing this large data set in order to detect small differences in metabolic profiles in combination with a large biological variation. Such metabolomics studies require a careful analytical and statistical protocol. The strategy included data preprocessing, data analysis, and validation of statistical models. After several data preprocessing steps, partial least-squares discriminant analysis (PLS-DA) was used for finding biomarkers. To validate the found biomarkers statistically, the PLS-DA models were validated by means of a permutation test, biomarker models, and noninformative models. Univariate plots of potential biomarkers were used to obtain insight in up- or downregulation. The strategy proposed proved to be applicable for dealing with large-scale human metabolomics studies. © 2006 American Chemical Society.

As part of a project funded by the European Commission (EC) for the development and evaluation of multiresidue methods for analysis of drinking and related waters, 17 European laboratories evaluated a method using styrene-divinylbenzene copolymer solid-phase extraction followed by liquid chromatography with diode array detection. The main aim of the study was to evaluate whether the method meets the requirements of EC Drinking Water Directive 98/83 in terms of accuracy, precision, and detection limit for 21 pesticides according to the following requirements: limit of detection,or =0.025 microg/L; accuracy expressed as recovery, between 75 and 125%; and precision expressed as repeatability relative standard deviation of the method,12.5%, and as reproducibility relative standard deviation of the method,25%. Analyses for unknown concentrations were performed with commercial bottled and tap waters. All laboratories were able to achieve detection limits of 0.01 microg/L for all pesticides except pirimicarb (0.02 microg/L). The criteria for repeatability were met for all compounds. Terbutryn in bottled water and carbendazim in tap water did not meet the criteria for reproducibility. In terms of accuracy, the method met the requirements for all pesticides in both matrixes, except for metamitron. However, several compounds (linuron, terbutryn, propazine, metobromuron, and isoproturon) showed recoveries slightly below 75%.