278 resultados para Gas Chromatography coupled with Nitrogen-Phosphorus Detector


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To enhance understanding of the metabolic indicators of type 2 diabetes mellitus (T2DM) disease pathogenesis and progression, the urinary metabolomes of well characterized rhesus macaques (normal or spontaneously and naturally diabetic) were examined. High-resolution ultra-performance liquid chromatography coupled with the accurate mass determination of time-of-flight mass spectrometry was used to analyze spot urine samples from normal (n = 10) and T2DM (n = 11) male monkeys. The machine-learning algorithm random forests classified urine samples as either from normal or T2DM monkeys. The metabolites important for developing the classifier were further examined for their biological significance. Random forests models had a misclassification error of less than 5%. Metabolites were identified based on accurate masses (<10 ppm) and confirmed by tandem mass spectrometry of authentic compounds. Urinary compounds significantly increased (p < 0.05) in the T2DM when compared with the normal group included glycine betaine (9-fold), citric acid (2.8-fold), kynurenic acid (1.8-fold), glucose (68-fold), and pipecolic acid (6.5-fold). When compared with the conventional definition of T2DM, the metabolites were also useful in defining the T2DM condition, and the urinary elevations in glycine betaine and pipecolic acid (as well as proline) indicated defective re-absorption in the kidney proximal tubules by SLC6A20, a Na(+)-dependent transporter. The mRNA levels of SLC6A20 were significantly reduced in the kidneys of monkeys with T2DM. These observations were validated in the db/db mouse model of T2DM. This study provides convincing evidence of the power of metabolomics for identifying functional changes at many levels in the omics pipeline.

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ThioTEPA, an alkylating agent with anti-tumor activity, has been used as an effective anticancer drug since the 1950s. However, a complete understanding of how its alkylating activity relates to clinical efficacy has not been achieved, the total urinary excretion of thioTEPA and its metabolites is not resolved, and the mechanism of formation of the potentially toxic metabolites S-carboxymethylcysteine (SCMC) and thiodiglycolic acid (TDGA) remains unclear. In this study, the metabolism of thioTEPA in a mouse model was comprehensively investigated using ultra-performance liquid chromatography coupled with electrospray ionization quadrupole time-of-flight mass spectrometry (UPLC-ESI-QTOFMS) based-metabolomics. The nine metabolites identified in mouse urine suggest that thioTEPA underwent ring-opening, N-dechloroethylation, and conjugation reactions in vivo. SCMC and TDGA, two downstream thioTEPA metabolites, were produced from thioTEPA from two novel metabolites 1,2,3-trichloroTEPA (VII) and dechloroethyltrichloroTEPA (VIII). SCMC and TDGA excretion were increased about 4-fold and 2-fold, respectively, in urine following the thioTEPA treatment. The main mouse metabolites of thioTEPA in vivo were TEPA (II), monochloroTEPA (III) and thioTEPA-mercapturate (IV). In addition, five thioTEPA metabolites were detected in serum and all shared similar disposition. Although thioTEPA has a unique chemical structure which is not maintained in the majority of its metabolites, metabolomic analysis of its biotransformation greatly contributed to the investigation of thioTEPA metabolism in vivo, and provides useful information to understand comprehensively the pharmacological activity and potential toxicity of thioTEPA in the clinic.

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Procainamide, a type I antiarrhythmic agent, is used to treat a variety of atrial and ventricular dysrhythmias. It was reported that long-term therapy with procainamide may cause lupus erythematosus in 25-30% of patients. Interestingly, procainamide does not induce lupus erythematosus in mouse models. To explore the differences in this side-effect of procainamide between humans and mouse models, metabolomic analysis using ultra-performance liquid chromatography coupled with electrospray ionization quadrupole time-of-flight mass spectrometry (UPLC-ESI-QTOFMS) was conducted on urine samples from procainamide-treated humans, CYP2D6-humanized mice, and wild-type mice. Thirteen urinary procainamide metabolites, including nine novel metabolites, derived from P450-dependent, FMO-dependent oxidations and acylation reactions, were identified and structurally elucidated. In vivo metabolism of procainamide in CYP2D6-humanized mice as well as in vitro incubations with microsomes and recombinant P450s suggested that human CYP2D6 plays a major role in procainamide metabolism. Significant differences in N-acylation and N-oxidation of the drug between humans and mice largely account for the interspecies differences in procainamide metabolism. Significant levels of the novel N-oxide metabolites produced by FMO1 and FMO3 in humans might be associated with the development of procainamide-induced systemic lupus erythematosus. Observations based on this metabolomic study offer clues to understanding procainamide-induced lupus in humans and the effect of P450s and FMOs on procainamide N-oxidation.

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Global transcriptomic and proteomic profiling platforms have yielded important insights into the complex response to ionizing radiation (IR). Nonetheless, little is known about the ways in which small cellular metabolite concentrations change in response to IR. Here, a metabolomics approach using ultraperformance liquid chromatography coupled with electrospray time-of-flight mass spectrometry was used to profile, over time, the hydrophilic metabolome of TK6 cells exposed to IR doses ranging from 0.5 to 8.0 Gy. Multivariate data analysis of the positive ions revealed dose- and time-dependent clustering of the irradiated cells and identified certain constituents of the water-soluble metabolome as being significantly depleted as early as 1 h after IR. Tandem mass spectrometry was used to confirm metabolite identity. Many of the depleted metabolites are associated with oxidative stress and DNA repair pathways. Included are reduced glutathione, adenosine monophosphate, nicotinamide adenine dinucleotide, and spermine. Similar measurements were performed with a transformed fibroblast cell line, BJ, and it was found that a subset of the identified TK6 metabolites were effective in IR dose discrimination. The GEDI (Gene Expression Dynamics Inspector) algorithm, which is based on self-organizing maps, was used to visualize dynamic global changes in the TK6 metabolome that resulted from IR. It revealed dose-dependent clustering of ions sharing the same trends in concentration change across radiation doses. "Radiation metabolomics," the application of metabolomic analysis to the field of radiobiology, promises to increase our understanding of cellular responses to stressors such as radiation.

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Trichloroethylene (TCE)-induced liver toxicity and carcinogenesis is believed to be mediated in part by activation of the peroxisome proliferator-activated receptor α (PPARα). However, the contribution of the two TCE metabolites, dichloroacetate (DCA) and trichloroacetate (TCA) to the toxicity of TCE, remains unclear. The aim of the present study was to determine the metabolite profiles in serum and urine upon exposure of mice to TCE, to aid in determining the metabolic response to TCE exposure and the contribution of DCA and TCA to TCE toxicity. C57BL/6 mice were administered TCE, TCA, or DCA, and urine and serum subjected to ultra-performance liquid chromatography coupled with electrospray ionization quadrupole time-of-flight mass spectrometry (UPLC-ESI-QTOFMS)-based global metabolomics analysis. The ions were identified through searching metabolomics databases and by comparison with authentic standards, and quantitated using multiple reactions monitoring. Quantitative polymerase chain reaction of mRNA, biochemical analysis, and liver histology were also performed. TCE exposure resulted in a decrease in urine of metabolites involved in fatty acid metabolism, resulting from altered expression of PPARα target genes. TCE treatment also induced altered phospholipid homeostasis in serum, as revealed by increased serum lysophosphatidylcholine 18:0 and 18:1, and phosphatidylcholine metabolites. TCA administration revealed similar metabolite profiles in urine and serum upon TCE exposure, which correlated with a more robust induction of PPARα target gene expression associated with TCA than DCA treatment. These data show the metabolic response to TCE exposure and demonstrate that TCA is the major contributor to TCE-induced metabolite alterations observed in urine and serum.

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Praziquantel (PZQ), prescribed as a racemic mixture, is the most readily available drug to treat schistosomiasis. In the present study, ultra-performance liquid chromatography coupled with electrospray ionization quadrupole time-of-flight mass spectrometry (UPLC-ESI-QTOFMS) based metabolomics was employed to decipher the metabolic pathways and enantioselective metabolic differences of PZQ. Many phase I and four new phase II metabolites were found in urine and feces samples of mice 24h after dosing, indicating that the major metabolic reactions encompassed oxidation, dehydrogenation, and glucuronidation. Differences in the formation of all these metabolites were observed between (R)-PZQ and (S)-PZQ. In an in vitro phase I incubation system, the major involvement of CYP3A, CYP2C9, and CYP2C19 in the metabolism of PZQ, and CYP3A, CYP2C9, and CYP2C19 exhibited different catalytic activity toward the PZQ enantiomers. Apparent Km and Vmax differences were observed in the catalytic formation of three mono-oxidized metabolites by CYP2C9 and CYP3A4 further supporting the metabolic differences for PZQ enantiomers. Molecular docking showed that chirality resulted in differences in substrate location and conformation, which likely accounts for the metabolic differences. In conclusion, in silico, in vitro, and in vivo methods revealed the enantioselective metabolic profile of praziquantel.

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Stable carbon isotope analysis of methane (delta C-13 of CH4) on atmospheric samples is one key method to constrain the current and past atmospheric CH4 budget. A frequently applied measurement technique is gas chromatography (GC) isotope ratio mass spectrometry (IRMS) coupled to a combustion-preconcentration unit. This report shows that the atmospheric trace gas krypton (Kr) can severely interfere during the mass spectrometric measurement, leading to significant biases in delta C-13 of CH4, if krypton is not sufficiently separated during the analysis. According to our experiments, the krypton interference is likely composed of two individual effects, with the lateral tailing of the doubly charged Kr-86 peak affecting the neighbouring m/z 44 and partially the m/z 45 Faraday cups. Additionally, a broad signal affecting m/z 45 and especially m/z 46 is assumed to result from scattered ions of singly charged krypton. The introduced bias in the measured isotope ratios is dependent on the chromatographic separation, the krypton-to-CH4 mixing ratio in the sample, the focusing of the mass spectrometer as well as the detector configuration and can amount to up to several per mil in delta C-13. Apart from technical solutions to avoid this interference, we present correction routines to a posteriori remove the bias.

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In preparation for the Russian Luna-Resurs mission we combined our compact time-of-flight mass spectrometer (TOF-MS) with a chemical pre-separation of the species by gas chromatography (GC). Coupled measurements with both instruments were successfully performed with the prototype of the mass spectrometer and a flight-like gas chromatograph. The system was tested with two test gas mixtures, a mixture of hydrocarbons and a mixture of noble gases. Due to its capability to record mass spectra over the full mass range at once with high sensitivity and a dynamic range of up to 10(6) within 1 s, the TOF-MS system is a valuable extension of the GC analytical system. Based on the measurements with calibration gases performed with the combined GC-MS prototype and under assumption of mean characteristics for the Moon's regolith, the detection limit for volatile species in a soil sample is estimated to 2.10(-10) by mass for hydrocarbons and 2.10(-9) by mass for noble gases. (C) 2015 Elsevier Ltd. All rights reserved.