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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.

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: 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.

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

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.

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.

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.