Lipidome Analysis in Brain and Peripheral Plasma Following Milk Fat Globule Membrane Supplementation in Rodents.

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 investigates bioavailability of bMFGM components after oral administration in two preclinical models to explore whether dietary bMFGM induces parallel changes to plasma and brain lipidomes. METHODS AND RESULTS: Transgenic APOE*3.Leiden mice (n = 18 per group) and Sprague-Dawley rats (n = 12 per group) are fed bMFGM-enriched (MFGM+) or Control diet, followed by phospholipid profile-determination in plasma, hippocampus, and prefrontal cortex tissue by targeted mass spectrometry. Multivariate analysis of lipidomic profiles demonstrates a separation between MFGM+ and Control plasma 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]) are consistently higher in the circulation of MFGM+ groups. A similar trend is observed in rat prefrontal cortex, although no significant separation of the brain lipidome is demonstrated. CONCLUSION: bMFGM-enriched diet alters plasma phospholipid composition in rodents, predominantly increasing sphingomyelin levels in the systemic circulation with similar, but non-significant, trends in central brain regions. These changes may contribute to the beneficial effects of bMFGM on neurodevelopment during early life.

Metabolic Profiling Reveals Differences in Plasma Concentrations of Arabinose and Xylose after Consumption of Fiber-Rich Pasta and Wheat Bread with Differential Rates of Systemic Appearance of Exogenous Glucose in Healthy Men.

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/m2): 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.

Plasma metabolomics and proteomics profiling after a postprandial challenge reveal subtle diet effects on human metabolic status.

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.

Analytical error reduction using single point calibration for accurate and precise metabolomic phenotyping.

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.

Improving the analysis of designed studies by combining statistical modelling with study design information.

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.

Validation of transferability of DBA derivatization and LC-MS/MS determination method for isocyanates via an interlaboratory comparison.

An adapted method for the quantitative determination of isocyanates in air was implemented and validated in-house. The method was based on air sampling using an impinger flask containing di-n-butylamine (DBA) in toluene and a glass fibre filter in series. The DBA derivatives were determined using liquid chromatography and tandem mass spectrometry. Studied isocyanates were isophorone diisocyanate, isocyanic acid (ICA), methyl isocyanate, ethyl isocyanate, propyl isocyanate, hexamethylene diisocyanate (HDI), 2,6- and 2,4-toluene diisocyanate, 4,4'-methylene diphenyl diisocyanate (MDI), phenyl isocyanate (PhI), MDI oligomers and different HDI adducts. Monitoring of selected reactions resulted in quantifications with correlation coefficients >0.995, within-batch relative standard deviation (RSD) of repeatability was <13% for all analytes. Between-batch RSD (reproducibility) was determined for all the compounds with the exception of the adducts and oligomers and was also <13%. As an additional validation procedure, the method was evaluated by exchanging field (air) and standard samples between two laboratories. The RSDs observed by the two laboratories were comparable. The concentrations determined were between 80 and 120% of each other, depending on the analyte and the individual concentrations. The method was applied in a large field study on exposure of workers in car repair shops and industrial painters with >500 samples.

Lipidomic approach to evaluate rosuvastatin and atorvastatin at various dosages: investigating differential effects among statins.

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 (PCs) 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<0.01 for between-treatment comparison). Both atorvastatin and rosuvastatin lowered plasma SPMs concentrations, but atorvastatin demonstrated a more pronounced effect with the highest dose (p=0.03). Rosuvastatin resulted in a significantly more effective lowering of the [SPMs/(SPMs + PCs)] ratio than atorvastatin at any dose/timepoint (p<0.05), a ratio reported to be of clinical importance in coronary artery disease. CONCLUSIONS: The lipidomic technique has revealed that statins are different with regards to the effect on detailed lipid profile. The observed difference in lipids may be connected with different clinical outcomes as suggested by the [SPMs/(SPMs + PCs)] ratio.

Quantitative profiling of bile acids in biofluids and tissues based on accurate mass high resolution LC-FT-MS: compound class targeting in a metabolomics workflow.

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 microM 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.

Correlation network analysis for data integration and biomarker selection.

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 detectable 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.

Large-scale human metabolomics studies: a strategy for data (pre-) processing and validation.

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.

Inhalation exposure to isocyanates of car body repair shop workers and industrial spray painters.

As part of a large-scale epidemiological study, occupational isocyanate exposure was assessed in spray-painting environments. The aim was to assess which compounds contribute to isocyanate exposure in car body repair shops and industrial painting companies, and to identify tasks with high risk of isocyanate exposure. Mainly personal task-based samples (n = 566) were collected from 24 car body repair shops and five industrial painting companies using impingers with DBA in toluene. Samples were analysed by LC-MS for isocyanate monomers, oligomers and products of thermal degradation. From the 23 analysed compounds, 20 were detected. Exploratory factor analysis resulted in a HDI, TDI and MDI factor with the thermal degradation products divided over the TDI and MDI factors. The HDI factor mainly consisted of HDI oligomers and was dominant in frequency and exposure levels in both industries. Spray painting of PU lacquers resulted in the highest exposures for the HDI factor (

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 sampling.

Multiresidue determination of pesticides in drinking and related waters by solid-phase extraction and liquid chromatography with ultraviolet detection: interlaboratory study.

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%.