When quantifying information in metabolomics, the results are often expressed as data carrying only relative information. Vectors of these data have positive components, and the only relevant information is contained in the ratios between their parts; such observations are called compositional data. The aim of the paper is to demonstrate how partial least squares discriminant analysis (PLS‐DA)—the most widely used method in chemometrics for multivariate classification—can be applied to compositional data. Theoretical arguments are provided, and data sets from metabolomics are investigated. The data are related to the diagnosis of inherited metabolic disorders (IMDs). The first example analyzes the significance of the corresponding regression parameters (metabolites) using a small data set resulting from targeted metabolomics, where just a subset of potential markers is selected. The second example—the approach of untargeted metabolomics—was used for the analysis detecting almost 500 metabolites. The significance of the metabolites is investigated by applying PLS‐DA, accommodated according to a compositional approach. The significance of important metabolites (markers of diseases) is more clearly visible with the compositional method in both examples. Also, cross‐validation methods lead to better results in case of using the compositional approach. Copyright © 2014 John Wiley & Sons, Ltd.

Clinical metabolic phenotyping employs metabolomics and lipidomics to detect and measure hundreds to thousands of metabolites and lipids within human samples. This approach aims to identify metabolite and lipid changes...

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Arachidonic acid (AA) and docosahexaenoic acid (DHA) play important roles in inflammation and disease progression, where AA is viewed as proinflammatory and DHA as antiinflammatory. We observe in our model of allergic asthma that the AA/DHA ratio is significantly skewed in a proinflammatory direction. Fenretinide, a vitamin A derivative, has been shown to correct fatty acid imbalances in other diseases. Therefore, we explored if fenretinide can have a protective effect in allergic asthma. To accomplish this, we measured the levels of AA and DHA in the lungs of nonallergic, ovalbumin-induced allergic, and fenretinide-treated allergic mice. We also investigated the effect of allergic asthma and fenretinide treatment on markers of oxidative stress, levels of metabolites, IgE production, airway hyperresponsiveness, and histological changes. Our data demonstrate that treatment of allergen-sensitized mice with fenretinide before allergen challenge prevents ovalbumin-induced changes in the AA/DHA ratio. The levels of several metabolites, such as serotonin, and markers of cellular stress, which are increased after ovalbumin challenge, are also controlled by fenretinide treatment. We observed the protective effect of fenretinide against ovalbumin-induced airway hyperresponsiveness and inflammation in the lungs, illustrated by a complete block in the infiltration of inflammatory cells to the airways and dramatically diminished goblet cell proliferation, even though IgE remained high. Our results demonstrate that fenretinide is an effective agent targeting inflammation, oxidation, and lung pathology observed in allergic asthma.

Modern separation methods in conjunction with high-resolution accurate mass (HRAM) spectrometry can provide an enormous number of features characterized by exact mass and chromatographic behavior. Higher mass resolving power usually requires longer scanning times, and thus fewer data points are acquired across the target peak. This could cause difficulties for quantification, feature detection and deconvolution. The aim of this work was to describe the influence of mass spectrometry resolving power on profiling metabolomics experiments. From metabolic databases (HMDB, LipidMaps, KEGG), a list of compounds (41 474) was compiled and potential adducts and isotopes were calculated (622 110 features). The number of distinguishable masses was calculated for up to 3840k resolution. To evaluate these models, human plasma samples were analyzed by LC-HRMS on an Orbitrap Elite hybrid mass spectrometer (Thermo Fisher Scientific, CA, USA) at resolving power settings of 15k (7.8 Hz) up to a maximum of 480k (1.2 Hz). Software XCMS 1.44, MZmine 2.13.1, and Compound Discoverer 2.0.0.303 were used for evaluation. In plasma samples, the number of detected features increased sharply up to 60k in both positive and negative mode. However, beyond these values, it either flattened out or decreased owing to technical limitations. In conclusion, the most effective mass resolving powers for profiling analyses of metabolite rich biofluids on the Orbitrap Elite were around 60 000-120 000 fwhm to retrieve the highest amount of information. The region between 400-800 m/z was influenced the most by resolution.

Characterisation of animal models of diabetic cardiomyopathy may help unravel new molecular targets for therapy. Long-living individuals are protected from the adverse influence of diabetes on the heart, and the transfer of a longevity-associated variant (LAV) of the human BPIFB4 gene protects cardiac function in the db/db mouse model. This study aimed to determine the effect of LAV-BPIFB4 therapy on the metabolic phenotype (ultra-high-performance liquid chromatography-mass spectrometry, UHPLC-MS) and cardiac transcriptome (next-generation RNAseq) in db/db mice. UHPLC-MS showed that 493 cardiac metabolites were differentially modulated in diabetic compared with non-diabetic mice, mainly related to lipid metabolism. Moreover, only 3 out of 63 metabolites influenced by LAV-BPIFB4 therapy in diabetic hearts showed a reversion from the diabetic towards the non-diabetic phenotype. RNAseq showed 60 genes were differentially expressed in hearts of diabetic and non-diabetic mice. The contrast between LAV-BPIFB4- and vehicle-treated diabetic hearts revealed eight genes differentially expressed, mainly associated with mitochondrial and metabolic function. Bioinformatic analysis indicated that LAV-BPIFB4 re-programmed the heart transcriptome and metabolome rather than reverting it to a non-diabetic phenotype. Beside illustrating global metabolic and expressional changes in diabetic heart, our findings pinpoint subtle changes in mitochondrial-related proteins and lipid metabolism that could contribute to LAV-BPIFB4-induced cardio-protection in a murine model of type-2 diabetes.

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