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The foie gras is an emblematic product of French gastronomy composed of waterfowl fatty liver. The organoleptic qualities of this product depend on the liver characteristics such as liver weight (LW) and technological yield (TY) at cooking. One of the main issues for producers is to classify the foie gras with high or low technological quality before cooking them. Thus the study aims at identifying biomarkers of these characteristics with non-invasive biomarkers in duck. 1H-NMR (nuclear magnetic resonance of the proton) analyses were performed on plasma of male mule ducks at different time points during the overfeeding period to obtain a large range of liver characteristics so as to identify plasmatic biomarkers of foie gras. We used two methods, one based on bucket data from the 1H-NMR spectra and another one based on the fingerprints of several metabolites. PLS analyses and Linear models were performed to identify biomarkers. We identified 18 biomarkers of liver weight and 15 biomarkers of technological yield. As these two quality parameters were strongly correlated (−0.82), 13 biomarkers were common. The lactate was the most important biomarker, the other were mainly amino acids. Contrary to the amino acids, the lactate increased with the liver weight and decreased with the technological yield. We also identified 5 biomarkers specific to LW (3 carbohydrates: glucuronic acid, mannose, sorbitol and 2 amino acids: glutamic acid and methionine) that were negatively correlated to liver weight. It was of main interest to identify 2 biomarkers specific to the technological yield. Contrary to the isovaleric acid, the valine was negatively correlated to the technological yield. [ABSTRACT FROM AUTHOR]

Introduction: The salivary metabolome has been increasingly studied over the past ten years due to the potential of saliva as a non-invasive source of biomarkers. However, although saliva has been studied in relation to various diseases, its dynamic evolution during life is not known. This is particularly true for the first months of life. Infancy is indeed a critical period during which numerous behavioural and physiological events occur, such as dietary transitions and tooth eruption, which can lead to important biological modifications in the oral cavity. Objectives: The aim of this work was therefore to study the evolution of the salivary metabolome during the first months of life by 1H NMR. Methods: Saliva of 32 infants with different milk feeding histories (breast vs formula) was collected at 6 stages, including 3 months old, 15 days before the onset of complementary feeding (CF), approximately 15 days after the onset of CF, approximately 21 days after the onset of CF and at approximately 11 and 15 months, and analysed. Results: The longitudinal analysis showed a significant modification of the profiles of 18 metabolites over time; 14 presented an increase in abundance whereas 4 presented a decrease. These modifications seemed to be linked, for the most part, to an increase in oral microbial metabolism. Milk feeding history during the first months of life had no effect on metabolites. Conclusion: This work shows that the salivary metabolome should be considered when studying the changes occurring during infancy. [ABSTRACT FROM AUTHOR]

BACKGROUND: Epidemiological evidence suggests a link between pesticide exposure and the development of metabolic diseases. However, most experimental studies have evaluated the metabolic effects of pesticides using individual molecules, often at nonrelevant doses or in combination with other risk factors such as high-fat diets. OBJECTIVES: We aimed to evaluate, in mice, the metabolic consequences of chronic dietary exposure to a pesticide mixture at nontoxic doses, relevant to consumers' risk assessment. METHODS: A mixture of six pesticides commonly used in France, i.e., boscalid, captan, chlorpyrifos, thiofanate, thiacloprid, and ziram, was incorporated in a standard chow at doses exposing mice to the tolerable daily intake (TDI) of each pesticide. Wild-type (WT) and constitutive androstane receptor-deficient (CAR-/-) male and female mice were exposed for 52 wk. We assessed metabolic parameters [body weight (BW), food and water consumption, glucose tolerance, urinary metabolome] throughout the experiment. At the end of the experiment, we evaluated liver metabolism (histology, transcriptomics, metabolomics, lipidomics) and pesticide detoxification using liquid chromatography-mass spectrometry (LC-MS). RESULTS: Compared to those fed control chow, WT male mice fed pesticide chow had greater BW gain and more adiposity. Moreover, these WT males fed pesticide chow exhibited characteristics of hepatic steatosis and glucose intolerance, which were not observed in those fed control chow. WT exposed female mice exhibited fasting hyperglycemia, higher reduced glutathione (GSH):oxidized glutathione (GSSG) liver ratio and perturbations of gut microbiota-related urinary metabolites compared to WT mice fed control chow. When we performed these experiments on CAR-/- mice, pesticide-exposed CAR-/- males did not exhibit BW gain or changes in glucose metabolism compared to the CAR-/- males fed control chow. Moreover, CAR-/- females fed pesticide chow exhibited pesticide toxicity with higher BWs and mortality rate compared to the CAR-/- females fed control chow. CONCLUSIONS: To our knowledge, we are the first to demonstrate a sexually dimorphic obesogenic and diabetogenic effect of chronic dietary exposure to a common mixture of pesticides at TDI levels, and to provide evidence for a partial role for CAR in an in vivo mouse model. This raises questions about the relevance of TDI for individual pesticides when present in a mixture. [ABSTRACT FROM AUTHOR]

Metabolome profiling in biological fluids is an interesting approach for exploring markers of methane emissions in ruminants. In this study, a multiplatform metabolomics approach was used for investigating changes in milk metabolic profiles related to methanogenesis in dairy cows. For this purpose, 25 primiparous Holstein cows at similar lactation stage were fed the same diet supplemented with (treated, n = 12) or without (control, n = 13) a specific antimethanogenic additive that reduced enteric methane production by 23% with no changes in intake, milk production, and health status. The study lasted 6 wk, with sampling and measures performed in wk 5 and 6. Milk samples were analyzed using 4 complementary analytical methods, including 2 untargeted (nuclear magnetic resonance and liquid chromatography coupled to a quadrupole-time-of-flight mass spectrometer) and 2 targeted (liquid chromatography-tandem mass spectrometry and gas chromatography coupled to a flame ionization detector) approaches. After filtration, variable selection and normalization data from each analytical platform were then analyzed using multivariate orthogonal partial least square discriminant analysis. All 4 analytical methods were able to differentiate cows from treated and control groups. Overall, 38 discriminant metabolites were identified, which affected 10 metabolic pathways including methane metabolism. Some of these metabolites such as dimethylsulfoxide, dimethylsulfone, and citramalic acid, detected by nuclear magnetic resonance or liquid chromatography-mass spectrometry methods, originated from the rumen microbiota or had a microbial-host animal co-metabolism that could be associated with methanogenesis. Also, discriminant milk fatty acids detected by targeted gas chromatography were mostly of ruminal microbial origin. Other metabolites and metabolic pathways significantly affected were associated with AA metabolism. These findings provide new insight on the potential role of milk metabolites as indicators of enteric methane modifications in dairy cows. [ABSTRACT FROM AUTHOR]

Motivation In metabolomics, the detection of new biomarkers from Nuclear Magnetic Resonance (NMR) spectra is a promising approach. However, this analysis remains difficult due to the lack of a whole workflow that handles spectra pre-processing, automatic identification and quantification of metabolites and statistical analyses, in a reproducible way. Results We present ASICS, an R package that contains a complete workflow to analyse spectra from NMR experiments. It contains an automatic approach to identify and quantify metabolites in a complex mixture spectrum and uses the results of the quantification in untargeted and targeted statistical analyses. ASICS was shown to improve the precision of quantification in comparison to existing methods on two independent datasets. In addition, ASICS successfully recovered most metabolites that were found important to explain a two level condition describing the samples by a manual and expert analysis based on bucketing. It also found new relevant metabolites involved in metabolic pathways related to risk factors associated with the condition. Availability and implementation ASICS is distributed as an R package, available on Bioconductor. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

In this work, clay‐Na particles are used as the adsorbent for the solid‐phase extraction of acidic compounds. The novel sorbent under study is based on high‐specific surface area, cation‐exchange capacity designed specifically to offer ion‐exchange properties with the goal being to selectively extract a group of acidic compounds. The effects of the extraction parameters including extraction elution solvent, sample volume and pH. In optimum conditions, the repeatability for one fiber (n = 3), expressed as % relative standard deviation, was between 0.3 and 4.3% for the acid compounds. The detection limits for the studied acidic compounds were between 0.1–0.6 μg/L. The developed method offers the advantages of being simple to use and having a low cost of equipment. [ABSTRACT FROM AUTHOR]

Fungal secondary metabolites are defined by bioactive properties that ensure adaptation of the fungus to its environment. Although some of these natural products are promising sources of new lead compounds especially for the pharmaceutical industry, others pose risks to human and animal health. The identification of secondary metabolites is critical to assessing both the utility and risks of these compounds. Since fungi present biological specificities different from other microorganisms, this review covers the different strategies specifically used in fungal studies to perform this critical identification. Strategies focused on the direct detection of the secondary metabolites are firstly reported. Particularly, advances in high-throughput untargeted metabolomics have led to the generation of large datasets whose exploitation and interpretation generally require bioinformatics tools. Then, the genome-based methods used to study the entire fungal metabolic potential are reported. Transcriptomic and proteomic tools used in the discovery of fungal secondary metabolites are presented as links between genomic methods and metabolomic experiments. Finally, the influence of the culture environment on the synthesis of secondary metabolites by fungi is highlighted as a major factor to consider in research on fungal secondary metabolites. Through this review, we seek to emphasize that the discovery of natural products should integrate all of these valuable tools. Attention is also drawn to emerging technologies that will certainly revolutionize fungal research and to the use of computational tools that are necessary but whose results should be interpreted carefully. [ABSTRACT FROM AUTHOR]

Rationale High-throughput analyses require an overall analytical workflow including not only a robust and high-speed technical platform, but also dedicated data-processing tools able to extract the relevant information. This work aimed at evaluating post-acquisition data-mining tools for selective extraction of metabolite species from direct introduction high-resolution mass spectrometry data. Methods Investigations were performed on spectral data in which seven metabolites of vinclozolin, a dicarboximide fungicide containing two chloride atoms, were previously manually identified. The spectral data obtained from direct introduction (DI) and high-resolution mass spectrometry (HRMS) detection were post-processed by plotting the mass defect profiles and applying various data-filtering methods based on accurate mass values. Results Exploration of mass defect profiles highlighted, in a specific plotting region, the presence of compounds containing common chemical elements and pairs of conjugated and non-conjugated metabolites resulting from classical metabolic pathways. Additionally, the judicious application of mass defect and/or isotope pattern filters removed many interfering ions from DI-HRMS data, greatly facilitating the detection of vinclozolin metabolites. Compared with previous results obtained by manual data treatment, three additional metabolites of vinclozolin were detected and putatively annotated. Conclusions Tracking simultaneously several specific species could be efficiently performed using data-mining tools based on accurate mass values. The selectivity of the data extraction was improved when the isotope filter was used for halogenated compounds, facilitating metabolite ion detection even for low-abundance species. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Lipids are key molecules in various biological processes, thus their quantification is a crucial point in a lot of studies and should be taken into account in lipidomics development. This family is complex and presents a very large diversity of structures, so analyzing and quantifying all this diversity is a real challenge. In this review, the different techniques to analyze lipids will be presented: from nuclear magnetic resonance (NMR) to mass spectrometry (with and without chromatography) including universal detectors. First of all, the state of the art of quantification, with the definitions of terms and protocol standardization, will be presented with quantitative lipidomics in mind, and then technical considerations and limitations of analytical chemistry's tools, such as NMR, mass spectrometry and universal detectors, will be discussed, particularly in terms of absolute quantification. [ABSTRACT FROM AUTHOR]