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Researchers have used whole-genome sequencing and gene expression profiling to identify genes associated with age, in the hope of understanding the underlying mechanisms of senescence. But there is a substantial gap from variation in gene sequences and expression levels to variation in age or life expectancy. In an attempt to bridge this gap, here we describe the effects of age, sex, genotype, and their interactions on high-sensitivity metabolomic profiles in the fruit fly, Drosophila melanogaster. Among the 6800 features analyzed, we found that over one-quarter of all metabolites were significantly associated with age, sex, genotype, or their interactions, and multivariate analysis shows that individual metabolomic profiles are highly predictive of these traits. Using a metabolomic equivalent of gene set enrichment analysis, we identified numerous metabolic pathways that were enriched among metabolites associated with age, sex, and genotype, including pathways involving sugar and glycerophospholipid metabolism, neurotransmitters, amino acids, and the carnitine shuttle. Our results suggest that high-sensitivity metabolomic studies have excellent potential not only to reveal mechanisms that lead to senescence, but also to help us understand differences in patterns of aging among genotypes and between males and females. [ABSTRACT FROM AUTHOR]

Inhalation of cadmium (Cd) is associated with lung diseases, but less is known concerning pulmonary effects of Cd found in the diet. Cd has a decades- long half-life in humans and significant bioaccumulation occurs with chronic dietary intake. We exposed mice to low-dose CdCl2 (10 mg/L in drinking water) for 20 weeks, which increased lung Cd to a level similar to that of nonoccupationally exposed adult humans. Cd-treated mice had increased airway hyperresponsiveness to methacholine challenge, and gene expression array showed that Cd altered the abundance of 443 mRNA transcripts in mouse lung. In contrast to higher doses, low-dose Cd did not elicit increased metallothionein transcripts in lung. To identify pathways most affected by Cd, gene set enrichment of transcripts was analyzed. Results showed that major inducible targets of low-dose Cd were neuronal receptors represented by enriched olfactory, glutamatergic, cholinergic, and serotonergic gene sets. Olfactory receptors regulate chemosensory function and airway hypersensitivity, and these gene sets were the most enriched. Targeted metabolomics analysis showed that Cd treatment also increased metabolites in pathways of glutamatergic (glutamate), serotonergic (tryptophan), cholinergic (choline), and catecholaminergic (tyrosine) receptors in the lung tissue. Protein abundance measurements showed that the glutamate receptor GRIN2A was increased in mouse lung tissue. Together, these results show that in mice, oral low-dose Cd increased lung Cd to levels comparable to humans, increased airway hyperresponsiveness and disrupted neuronal pathways regulating bronchial tone. Therefore, dietary Cd may promote or worsen airway hyperresponsiveness in multiple lung diseases including asthma. [ABSTRACT FROM AUTHOR]

The study of metabolism has had a long history. Metabolomics, a systems biology discipline representing analysis of known and unknown pathways of metabolism, has grown tremendously over the past 20 years. Because of its comprehensive nature, metabolomics requires careful consideration of the question(s) being asked, the scale needed to answer the question(s), collection and storage of the sample specimens, methods for extraction of the metabolites frombiologicalmatrices, the analyticalmethod(s) to be employed and the quality control of the analyses, how collected data are correlated, the statistical methods to determine metabolites undergoing significant change, putative identification ofmetabolites and the use of stable isotopes to aid in verifying metabolite identity and establishing pathway connections and fluxes. The National Institutes of Health Common Fund Metabolomics Program was established in 2012 to stimulate interest in the approaches and technologies of metabolomics. To deliver one of the program's goals, the University of Alabama at Birmingham has hosted an annual 4-day short course in metabolomics for faculty, postdoctoral fellows and graduate students from national and international institutions. This paper is the first part of a summary of the training materials presented in the course to be used as a resource for all those embarking on metabolomics research. The complete set of training materials including slide sets and videos can be viewed at http://www. uab.edu/proteomics/metabolomics/workshop/workshop_june_2015.php. [ABSTRACT FROM AUTHOR]

R2142 --> 699.10 --> High resolution mass spectrometry (HRMS) detects tens of thousands of unknown features in human plasma. Many of these features could be indicative of xenobiotic exposure, but identifying candidates poses a challenge due to low abundance and lack of authentic standards. Here we propose using co‐occurrence of metabolites as a criterion to aid identification of undocumented xenobiotic exposures and co‐exposures in humans. Plasma from 49 volunteer Emory University/Georgia Tech employees in a longitudinal study were analyzed with HRMS, and aligned features were arranged as nodes in networks connected according to feature co‐occurrence and common biotransformation mass differences, marking possible xenobiotic pathways. These networks were compared against curated metabolic profiles of xenobiotic reference material generated on an enzyme‐based biotransformation system (liver S9 fractions). Profile matches, coupled with available MS/MS, suggest confident exposure identification of zolpidem, dextromethorphan, nicotine, citalopram, caffeine and tramadol within the cohort. Further investigation of co‐occurrence networks also facilitated suggestion of possible xenobiotic co‐exposures beyond biotransformation pathways. In this cohort, Zolpidem metabolites co‐occurred with high abundance features having the the masses of melatonin and its metabolites, suggesting co‐exposure due to polypharmacy for a single condition. [ABSTRACT FROM AUTHOR]

This work confirms the new view of the initiation and propagation mechanism of the anionic polymerization previously proposed, based on the investigation of anionic bulk-polymerization of styrene and α-methyl styrene with the help of a self designed microflow device and characterized by GPC and in situ 7Li NMR. It was found that n-BuLi tended to form the hexameric-aggregated structure and even to form the huge aggregated structure based on the former. These aggregations of initiators could directly initiate the anionic polymerization and form the supramolecule aggregations. The supramolecule aggregations inevitably blocked the diffusion of the monomers to the ion-pairs and resulted in a stationary-conversion platform. Then the aggregators were dissociated completely into equal binary-aggregated species, and the polymerization continued again rapidly before the termination. Tetrahydrofuran (THF) acted as the electron donator, which could push the electron cloud to Li cation and make the aggregated ring of the active species rather loosened and facilitated the monomer to insert in. Therefore, a little THF can greatly promote the anionic polymerization. However, further addition of THF might block the channel between the ion-pairs and decrease the propagation rate. It was also found that the aggregated structure of the active species during the anionic polymerization only depends on the initiator aggregations which were formed before the polymerization. [ABSTRACT FROM AUTHOR]

R4277 --> Background: The diversity of chemicals detectable in human samples by high‐resolution mass spectrometry (HRMS) exceeds the number of readily available chemical standards, limiting our ability to identify minor metabolites of drugs and other xenobiotics in vivo. Standard workflows for identification of unknown metabolites typically begin by interpretation of ion dissociation spectra (MS/MS) and comparison with library spectra and/or authentic standards – many of which are not available and do not incorporate enzymatic precursor‐product relationships as identification criteria. Objective: To develop a high‐throughput strategy to generate human biotransformation products from diverse xenobiotic and drugs for analysis with LC‐HRMS. Hypothesis: We hypothesize that unidentified metabolites are generated from enzymatic reactions of known metabolites. Therefore, strategies to generate and characterize metabolic products for diverse arrays of xenobiotics will facilitate identification of metabolites in vivo. Methods: Here, we adopt a strategy to generate human biotransformation products of diverse xenobiotics in a high‐throughput 96‐well plate format using incubations with human liver S9 fractions. Extracts from these reactions were collected at 0 and 24 hour time points and analyzed using LC‐HRMS (Thermo Scientific Fusion/High‐Field Q‐Exactive) to characterize metabolic products generated in a time‐dependent manner. Data‐dependent MS/MS was performed to collect MS/MS spectra. Expected biotransformation products were characterized by retention time, accurate mass m/z (MS1), MS/MS, and an increase in signal intensity for predicted or previously unreported biotransformation products. Incubations with stable‐isotope precursors aided the identification of previously unreported biotransformation products. Results: Our data show that known and previously unidentified Phase 1 and Phase 2 metabolites were produced from a range of xenobiotics in a 96‐well format using human liver S9 fractions. S9 reaction extracts of 138 drug or xenobiotic precursors were analyzed generating a total of 502 validated metabolites. Selected metabolites were used to support their identification in mouse and human samples based on matched accurate mass MS1, retention time, and co‐occurrence with the parent xenobiotic and/or related metabolites in the samples. Conclusion: We have developed a scalable tool to generate metabolites from thousands of xenobiotics to facilitate identification of drug and other xenobiotic metabolites in human samples. [ABSTRACT FROM AUTHOR]

R4160 --> As an approach to identify unknown mass spectral features corresponding to xenobiotic metabolites and simultaneously identify the enzyme(s) responsible, we developed a panel of lentivirally transduced human hepatoma cell lines (Huh7) that express xenobiotic metabolizing enzymes. However, screening thousands of chemicals individually against 40‐50 different cell lines each would require immense analytical resources. We hypothesized that pooling samples can improve analytical efficiency and throughput without significantly affecting metabolite generation or detection, and designed two pooling strategies to generate and detect metabolites from 36 different chemicals (including a number of drugs whose metabolism is well characterized) and 8 cytochrome P450‐expressing HuH7 cell lines. In the pre‐pooling strategy, 36 chemicals were grouped into 12 pools of 6 randomized compounds, with each compound present in two separate pools. The 12 pools were each incubated with 8 different cell lines for 0 and 2h resulting in 192 samples for analyses. In the post‐pooling strategy, 36 chemicals were incubated individually with 8 different cell lines. After incubation, the 36 chemicals were pooled by the same system as the pre‐pooling strategy. 10 μL aliquots of sample extracts were analyzed using liquid chromatography (Dionex Ultimate 3000) and Fourier‐transform high‐resolution mass spectrometry (Thermo Scientific Fusion) in both HILIC+ and C18‐ modes. Expression of the P450 proteins was verified by immunoblotting. Results from the pre‐pooled samples showed that the anti‐cancer drug pazopanib was metabolized by the CYP3A4‐ and CYP2C8‐expressing cell lines. Bupropion and β‐naphthoflavone were metabolized mainly by CYP2B6 and CYP1A2, verifying that the cell lines express functional P450s. Next, we compared the metabolomic profiles of the xenobiotics from the two different pooling strategies. The post‐pooling strategy generally showed more metabolite generation, although it required 3 times as many biological sample incubations. Hydroxybupropion and β‐naphthoflavone dihydrodiol generation were similar between the two pooling strategies. Qualitatively, the generation of hydroxy trifluralin, a possible metabolite of the commonly used herbicide, showed no significant differences between the two pooling strategies, although the signal levels using pre‐pooling were 13 times higher than in post‐pooling samples. Thus, either pooling strategy can be used as screens to identify enzyme‐compound pair reactions and to guide subsequent metabolite generation and characterization. The presence of each xenobiotic in two pools increases stringency for qualifying a feature as a metabolite, while achieving the same confirmation as technical replicates. While the pre‐pooling strategy may be vulnerable to confounding effects of potential interactions among xenobiotics in a given pool, the presence of each substrate in two different pools mitigated this concern. [ABSTRACT FROM AUTHOR]