Mass spectrometry profiling reveals altered plasma levels of monohydroxy fatty acids and related lipids in healthy humans after controlled exposure to biodiesel exhaust.

Experimental human exposure studies are an effective tool to study adverse health effects from acute inhalation of particulate matter and other constituents of air pollution. In this randomized and double-blinded crossover study, we investigated the systemic effect on bioactive lipid metabolite levels after controlled biodiesel exhaust exposure of healthy humans and compared it to filtered air at a separate exposure occasion. Eicosanoids and other oxylipins, as well as endocannabinoids and related lipids, were quantified in plasma from 14 healthy volunteers at baseline and at three subsequent time points (2, 6, and 24 h) after 1 h exposure sessions. Protocols based on liquid chromatography (LC) coupled to tandem mass spectrometry (MS/MS) methods were developed to detect temporal changes in circulating levels after biodiesel exhaust exposure. The exhaust was generated by a diesel engine fed with an undiluted rapeseed methyl ester fuel. Among the 51 analyzed lipid metabolites, PGF2α, 9,10-DiHOME, 9-HODE, 5-HETE, 11-HETE, 12-HETE, and DEA displayed significant responsiveness to the biodiesel exhaust exposure as opposed to filtered air. Of these, 9-HODE and 5-HETE at 24 h survived the 10% false discovery rate cutoff (p < 0.003). Hence, the majority of the responsive lipid metabolites were monohydroxy fatty acids. We conclude that it is possible to detect alterations in circulating bioactive lipid metabolites in response to biodiesel exhaust exposure using LC-MS/MS, with emphasis on metabolites with inflammation related properties and implications on cardiovascular health and disease. These observations aid future investigations on air pollution effects, especially with regard to cardiovascular outcomes.

Mass spectrometry profiling of oxylipins, endocannabinoids, and N-acylethanolamines in human lung lavage fluids reveals responsiveness of prostaglandin E2 and associated lipid metabolites to biodiesel exhaust exposure.

The adverse effects of petrodiesel exhaust exposure on the cardiovascular and respiratory systems are well recognized. While biofuels such as rapeseed methyl ester (RME) biodiesel may have ecological advantages, the exhaust generated may cause adverse health effects. In the current study, we investigated the responses of bioactive lipid mediators in human airways after biodiesel exhaust exposure using lipidomic profiling methods. Lipid mediator levels in lung lavage were assessed following 1-h biodiesel exhaust (average particulate matter concentration, 159 µg/m3) or filtered air exposure in 15 healthy individuals in a double-blinded, randomized, controlled, crossover study design. Bronchoscopy was performed 6 h post exposure and lung lavage fluids, i.e., bronchial wash (BW) and bronchoalveolar lavage (BAL), were sequentially collected. Mass spectrometry methods were used to detect a wide array of oxylipins (including eicosanoids), endocannabinoids, N-acylethanolamines, and related lipid metabolites in the collected BW and BAL samples. Six lipids in the human lung lavage samples were altered following biodiesel exhaust exposure, three from BAL samples and three from BW samples. Of these, elevated levels of PGE2, 12,13-DiHOME, and 13-HODE, all of which were found in BAL samples, reached Bonferroni-corrected significance. This is the first study in humans reporting responses of bioactive lipids following biodiesel exhaust exposure and the most pronounced responses were seen in the more peripheral and alveolar lung compartments, reflected by BAL collection. Since the responsiveness and diagnostic value of a subset of the studied lipid metabolites were established in lavage fluids, we conclude that our mass spectrometry profiling method is useful to assess effects of human exposure to vehicle exhaust.

Multi-platform metabolomics assays for human lung lavage fluids in an air pollution exposure study.

Metabolomics protocols are used to comprehensively characterize the metabolite content of biological samples by exploiting cutting-edge analytical platforms, such as gas chromatography (GC) or liquid chromatography (LC) coupled to mass spectrometry (MS) assays, as well as nuclear magnetic resonance (NMR) assays. We have developed novel sample preparation procedures combined with GC-MS, LC-MS, and NMR metabolomics profiling for analyzing bronchial wash (BW) and bronchoalveolar lavage (BAL) fluid from 15 healthy volunteers following exposure to biodiesel exhaust and filtered air. Our aim was to investigate the responsiveness of metabolite profiles in the human lung to air pollution exposure derived from combustion of biofuels, such as rapeseed methyl ester biodiesel, which are increasingly being promoted as alternatives to conventional fossil fuels. Our multi-platform approach enabled us to detect the greatest number of unique metabolites yet reported in BW and BAL fluid (82 in total). All of the metabolomics assays indicated that the metabolite profiles of the BW and BAL fluids differed appreciably, with 46 metabolites showing significantly different levels in the corresponding lung compartments. Furthermore, the GC-MS assay revealed an effect of biodiesel exhaust exposure on the levels of 1-monostearylglycerol, sucrose, inosine, nonanoic acid, and ethanolamine (in BAL) and pentadecanoic acid (in BW), whereas the LC-MS assay indicated a shift in the levels of niacinamide (in BAL). The NMR assay only identified lactic acid (in BW) as being responsive to biodiesel exhaust exposure. Our findings demonstrate that the proposed multi-platform approach is useful for wide metabolomics screening of BW and BAL fluids and can facilitate elucidation of metabolites responsive to biodiesel exhaust exposure. Graphical Abstract Graphical abstract illustrating the study workflow. NMR Nuclear Magnetic Resonance, LC-TOFMS Liquid chromatography-Time Of Flight Mass Spectrometry, GC Gas Chromatography-Mass spectrometry.

Postprandial metabolomics: A pilot mass spectrometry and NMR study of the human plasma metabolome in response to a challenge meal.

The study of postprandial metabolism is relevant for understanding metabolic diseases and characterizing personal responses to diet. We combined three analytical platforms - gas chromatography-mass spectrometry (GC-MS), liquid chromatography-mass spectrometry (LC-MS) and nuclear magnetic resonance (NMR) - to validate a multi-platform approach for characterizing individual variation in the postprandial state. We analyzed the postprandial plasma metabolome by introducing, at three occasions, meal challenges on a usual diet, and 1.5 years later, on a modified background diet. The postprandial response was stable over time and largely independent of the background diet as revealed by all three analytical platforms. Coverage of the metabolome between NMR and GC-MS included more polar metabolites detectable only by NMR and more hydrophobic compounds detected by GC-MS. The variability across three separate testing occasions among the identified metabolites was in the range of 1.1-86% for GC-MS and 0.9-42% for NMR in the fasting state at baseline. For the LC-MS analysis, the coefficients of variation of the detected compounds in the fasting state at baseline were in the range of 2-97% for the positive and 4-69% for the negative mode. Multivariate analysis (MVA) of metabolites detected with GC-MS revealed that for both background diets, levels of postprandial amino acids and sugars increased whereas those of fatty acids decreased at 0.5 h after the meal was consumed, reflecting the expected response to the challenge meal. MVA of NMR data revealed increasing postprandial levels of amino acids and other organic acids together with decreasing levels of acetoacetate and 3-hydroxybutanoic acid, also independent of the background diet. Together these data show that the postprandial response to the same challenge meal was stable even though it was tested 1.5 years apart, and that it was largely independent of background diet. This work demonstrates the efficacy of a multi-platform metabolomics approach followed by multivariate and univariate data analysis for a broad-scale screen of the individual metabolome, particularly for studies using repeated measures to determine dietary response phenotype.

Lipid mediator profiles differ between lung compartments in asthmatic and healthy humans.

Oxylipins are oxidised fatty acids that can exert lipid mediator functions in inflammation, and several oxylipins derived from arachidonic acid are linked to asthma. This study quantified oxylipin profiles in different regions of the lung to obtain a broad-scale characterisation of the allergic asthmatic inflammation in relation to healthy individuals. Bronchoalveolar lavage fluid (BALF), bronchial wash fluid and endobronchial mucosal biopsies were collected from 16 healthy and 16 mildly allergic asthmatic individuals. Inflammatory cell counts, immunohistochemical staining and oxylipin profiling were performed. Univariate and multivariate statistics were employed to evaluate compartment-dependent and diagnosis-dependent oxylipin profiles in relation to other measured parameters. Multivariate modelling showed significantly different bronchial wash fluid and BALF oxylipin profiles in both groups (R(2)Y[cum]=0.822 and Q(2)[cum]=0.759). Total oxylipin concentrations and five individual oxylipins, primarily from the lipoxygenase (LOX) pathway of arachidonic and linoleic acid, were elevated in bronchial wash fluid from asthmatics compared to that from healthy controls, supported by immunohistochemical staining of 15-LOX-1 in the bronchial epithelium. No difference between the groups was found among BALF oxylipins. In conclusion, bronchial wash fluid and BALF contain distinct oxylipin profiles, which may have ramifications for the study of respiratory diseases. Specific protocols for sampling proximal and distal airways separately should be employed for lipid mediator studies.

Quantitative and qualitative prediction of corneal permeability for drug-like compounds.

A set of 69 drug-like compounds with corneal permeability was studied using quantitative and qualitative modeling techniques. Multiple linear regression (MLR) and multilayer perceptron neural network (MLP-NN) were used to develop quantitative relationships between the corneal permeability and seven molecular descriptors selected by stepwise MLR and sensitivity analysis methods. In order to evaluate the models, a leave many out cross-validation test was performed, which produced the statistic Q(2)=0.584 and SPRESS=0.378 for MLR and Q(2)=0.774 and SPRESS=0.087 for MLP-NN. The obtained results revealed the suitability of MLP-NN for the prediction of corneal permeability. The contribution of each descriptor to MLP-NN model was evaluated. It indicated the importance of the molecular volume and weight. The pattern recognition methods principal component analysis (PCA) and hierarchical clustering analysis (HCA) have been employed in order to investigate the possible qualitative relationships between the molecular descriptors and the corneal permeability. The PCA and HCA results showed that, the data set contains two groups. Then, the same descriptors used in quantitative modeling were considered as inputs of counter propagation neural network (CPNN) to classify the compounds into low permeable (LP) and very low permeable (VLP) categories in supervised manner. The overall classification non error rate was 95.7% and 95.4% for the training and prediction test sets, respectively. The results revealed the ability of CPNN to correctly recognize the compounds belonging to the categories. The proposed models can be successfully used to predict the corneal permeability values and to classify the compounds into LP and VLP ones.