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Related Articles 1H-NMR Metabolomics Analysis of the Effect of Rubusoside on Serum Metabolites of Golden Hamsters on a High-Fat Diet. Molecules. 2020 Mar 11;25(6): Authors: Li L, Jiang M, Li Y, Su J, Li L, Qu X, Fan L Abstract Rubusoside is a natural sweetener and the active component of Rubus suavissimus. The preventive and therapeutic effect of rubusoside on high-fat diet-induced (HFD) serum metabolite changes in golden hamsters was analyzed by 1H-NMR metabolomics to explore the underlying mechanism of lipid metabolism regulation. 1H-NMR serum metabolomics analyses revealed a disturbed amino acid-, sugar-, fat-, and energy metabolism in HFD animals. Animals supplemented with rubusoside can partly reverse the metabolism disorders induced by high-fat diet and exerted good anti-hypertriglyceridemia effect by intervening in some major metabolic pathways, involving amino acid metabolism, synthesis of ketone bodies, as well as choline and 4-hydroxyphenylacetate metabolism. This study indicates that rubusoside can interfere with and normalize high-fat diet-induced metabolic changes in serum and could provide a theoretical basis to establish rubusoside as a potentially therapeutic tool able to revert or prevent lipid metabolism disorders. PMID: 32168894 [PubMed - as supplied by publisher]

Related Articles An Untargeted Metabolomics Approach to Investigate the Metabolic Effect of Beetroot Juice Supplementation in Fencers-A Preliminary Study. Metabolites. 2020 Mar 11;10(3): Authors: Kozlowska L, Mizera O, Mroz A Abstract This study aimed at assessment of the long-term (4 weeks) metabolic effect of a diet with and without beetroot juice supplementation in fencers using the untargeted metabolomics method with the UPLC Q-TOF/MS system to carry out an analysis of urine samples. Ten women and 10 men underwent the cardiovascular fitness VO2max test at baseline-(B) and after two stages of implementation of the dietary recommendations-the first 4 weeks without beetroot juice (D) and the second with 26 g/d of freeze-dried beetroot juice supplementation (D&J). The urine samples were collected one hour after the VO2max test at B and after D and D&J. The meal before the VO2max test after D&J contained beetroot juice, whereas to the meal at B and after D maltodextrin was added. Changes in metabolites and VO2max were significant only for comparison of D versus D&J. During D and D&J, there were no significant changes in the physical activity level, body mass, and body composition. We observed significant changes in tyrosine and tryptophan metabolism, mainly associated with such neurotransmitter's metabolism as: Serotonin, noradrenaline, and adrenaline. Changes in signal intensity of bile acid, AICAR, and 4-Hydroxynonenal (peroxidation of polyunsaturated fatty acids product) were also observed. The obtained results indicate that long-term beetroot juice supplementation induces considerable changes in metabolism. PMID: 32168803 [PubMed - as supplied by publisher]

Related Articles α-Linolenic Acid-Rich Diet Influences Microbiota Composition and Villus Morphology of the Mouse Small Intestine. Nutrients. 2020 Mar 11;12(3): Authors: Todorov H, Kollar B, Bayer F, Brandão I, Mann A, Mohr J, Pontarollo G, Formes H, Stauber R, Kittner JM, Endres K, Watzer B, Nockher WA, Sommer F, Gerber S, Reinhardt C Abstract α-Linolenic acid (ALA) is well-known for its anti-inflammatory activity. In contrast, the influence of an ALA-rich diet on intestinal microbiota composition and its impact on small intestine morphology are not fully understood. In the current study, we kept adult C57BL/6J mice for 4 weeks on an ALA-rich or control diet. Characterization of the microbial composition of the small intestine revealed that the ALA diet was associated with an enrichment in Prevotella and Parabacteroides. In contrast, taxa belonging to the Firmicutes phylum, including Lactobacillus, Clostridium cluster XIVa, Lachnospiraceae and Streptococcus, had significantly lower abundance compared to control diet. Metagenome prediction indicated an enrichment in functional pathways such as bacterial secretion system in the ALA group, whereas the two-component system and ALA metabolism pathways were downregulated. We also observed increased levels of ALA and its metabolites eicosapentanoic and docosahexanoic acid, but reduced levels of arachidonic acid in the intestinal tissue of ALA-fed mice. Furthermore, intestinal morphology in the ALA group was characterized by elongated villus structures with increased counts of epithelial cells and reduced epithelial proliferation rate. Interestingly, the ALA diet reduced relative goblet and Paneth cell counts. Of note, high-fat Western-type diet feeding resulted in a comparable adaptation of the small intestine. Collectively, our study demonstrates the impact of ALA on the gut microbiome and reveals the nutritional regulation of gut morphology. PMID: 32168729 [PubMed - as supplied by publisher]

16 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results: metabolomics These pubmed results were generated on 2020/03/14PubMed comprises more than millions of citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

16 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results: metabolomics These pubmed results were generated on 2020/03/14PubMed comprises more than millions of citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

20 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results: metabolomics These pubmed results were generated on 2020/03/13PubMed comprises more than millions of citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

20 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results: metabolomics These pubmed results were generated on 2020/03/13PubMed comprises more than millions of citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

28 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results: metabolomics These pubmed results were generated on 2020/03/12PubMed comprises more than millions of citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

28 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results: metabolomics These pubmed results were generated on 2020/03/12PubMed comprises more than millions of citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

38 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results: metabolomics These pubmed results were generated on 2020/03/11PubMed comprises more than millions of citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

38 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results: metabolomics These pubmed results were generated on 2020/03/11PubMed comprises more than millions of citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

20 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results: metabolomics These pubmed results were generated on 2020/03/10PubMed comprises more than millions of citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

20 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results: metabolomics These pubmed results were generated on 2020/03/10PubMed comprises more than millions of citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

Software tools, databases and resources in metabolomics: updates from 2018 to 2019. Metabolomics. 2020 Mar 07;16(3):36 Authors: O'Shea K, Misra BB Abstract Metabolomics has evolved as a discipline from a discovery and functional genomics tool, and is now a cornerstone in the era of big data-driven precision medicine. Sample preparation strategies and analytical technologies have seen enormous growth, and keeping pace with data analytics is challenging, to say the least. This review introduces and briefly presents around 100 metabolomics software resources, tools, databases, and other utilities that have surfaced or have improved in 2019. Table 1 provides the computational dependencies of the tools, categorizes the resources based on utility and ease of use, and provides hyperlinks to webpages where the tools can be downloaded or used. This review intends to keep the community of metabolomics researchers up to date with all the software tools, resources, and databases developed in 2019, in one place. PMID: 32146531 [PubMed - as supplied by publisher]

Metabolomic study reveals key metabolic adjustments in the xerohalophyte Salvadora persica L. during adaptation to water deficit and subsequent recovery conditions. Plant Physiol Biochem. 2020 Feb 28;150:180-195 Authors: Rangani J, Panda A, Parida AK Abstract Water deficit severely limits productivity of plants, and pose a major threat to modern agriculture system. Therefore, understanding drought adaptive mechanisms in drought-tolerant plants is imperative to formulate strategies for development of desiccation tolerance in crop plants. In present investigation, metabolic profiling employing GC-QTOF-MS/MS and HPLC-DAD was carried out to evaluate metabolic adjustments under drought stress in the xero-halophyte Salvadora persica. The metabolite profiling identified a total of 68 metabolites in S. persica leaf, including organic acids, amino acids, sugars, sugar alcohols, hormones, and polyphenols. The results showed that higher cellular osmolality under drought stress was accompanied by accumulations of several osmoprotectants like sugars and polyols (sucrose, glucose, mannose, galactose, erythrose, sorbose, glycerol, and myoinositol), organic acids (galactaric acid, tartaric acid, malic acid, oxalic acid, and citric acid), and amino acids (alanine, phenylalanine, tyrosine). Upregulation of ABA and JA support to achieve early drought tolerance in S. persica. Moreover, accumulation of coumarin, gallic acid, and chlorogenic acid provide antioxidative defense to S. persica. KEGG pathway enrichment analysis showed that altered metabolites were associated with starch and sucrose metabolism, galactose metabolism, inositol phosphate metabolism, and phenylalanine metabolism. While during recovery, metabolites associated with lysine biosynthesis and alanine, aspartate and glutamate metabolism were significantly altered. The results of the present study imply that coordinated regulations between various metabolites, metabolic processes, and pathways empower the xerohalophyte S. persica to adapt under drought environment. The knowledge from this study will enable the development of drought tolerance in crops using genetic engineering and breeding approaches. PMID: 32146282 [PubMed - as supplied by publisher]

Complete metabolic study by dibutyl phthalate degrading Pseudomonas sp. DNB-S1. Ecotoxicol Environ Saf. 2020 Mar 05;194:110378 Authors: Yu H, Wang L, Lin Y, Liu W, Tuyiringire D, Jiao Y, Zhang L, Meng Q, Zhang Y Abstract The primary purpose of this study was to systematically explore the complete metabolic pathway and tolerance mechanism of strain DNB-S1 to dibutyl phthalate (DBP), and the effect of DBP on energy metabolism of DNB-S1. Here, DNB-S1, a strain of Pseudomonas sp. that was highly effective in degrading DBP, was identified, and differentially expressed metabolites and metabolic networks of DBP were studied. The results showed that the differentially expressed metabolites were mainly aromatic compounds and lipid compounds, with only a few toxic intermediate metabolites. It speculated that phthalic acid, salicylic acid, 3-hydroxybenzoate acid, 3-Carboxy-cis, cis-muconate, fumarypyravate were intermediate metabolites of DBP. Their up-regulation indicated that there were two metabolic pathways in the degradation of DBP (protocatechuate pathway and gentisate pathway), which had been verified by peak changes at 290 nm, 320 nm, 330 nm, and 375 nm in the enzymatic method. Also, aspartate, GSH, and other metabolites were up-regulation, indicating that DNB-S1 had a high tolerance to DBP and maintained cell homeostasis, which was also one of the essential reasons to ensure the efficient degradation of DBP. Altogether, this study firstly proposed two pathways to degrade DBP and comprehensively explored the effect of DBP on the metabolic function of DNB-S1, which enriched the study of microbial metabolism of organic pollutants, and which provided a basis for the application of metabolomics. PMID: 32146194 [PubMed - as supplied by publisher]

Related Articles 1H-NMR metabolomics response to a realistic diet contamination with the mycotoxin deoxynivalenol: Effect of probiotics supplementation. Food Chem Toxicol. 2020 Mar 04;:111222 Authors: Alassane-Kpembi I, Canlet C, Tremblay-Franco M, Jourdan F, Chalzaviel M, Pinton P, Cossalter AM, Achard C, Castex M, Combes S, Bracarense APL, Oswald IP Abstract Low-level contamination of food and feed by deoxynivalenol (DON) is unavoidable. We investigated the effects of subclinical treatment with DON, and supplementation with probiotic yeast Saccharomyces cerevisiae boulardii I1079 as a preventive strategy in piglets. Thirty-six animals were randomly assigned to either a control diet, a diet contaminated with DON (3 mg/kg), a diet supplemented with yeast (4 × 109 CFU/kg), or a DON-contaminated diet supplemented with yeast, for four weeks. Plasma and tissue samples were collected for biochemical analysis, 1H-NMR untargeted metabolomics, and histology. DON induced no significant modifications in biochemical parameters. However, lesion scores were higher and metabolomics highlighted alterations of amino acid and 2-oxocarboxylic acid metabolism. Administering yeast affected aminoacyl-tRNA synthesis and amino acid and glycerophospholipid metabolism. Yeast supplementation of piglets exposed to DON prevented histological alterations, and partial least square discriminant analysis emphasised similarity between the metabolic profiles of their plasma and that of the control group. The effect on liver metabolome remained marginal, indicating that the toxicity of the mycotoxin was not eliminated. These findings show that the 1H-NMR metabolomics profile is a reliable biomarker to assess subclinical exposure to DON, and that supplementation with S. cerevisiae boulardii increases the resilience of piglets to this mycotoxin. PMID: 32145353 [PubMed - as supplied by publisher]

Related Articles Enhanced pseudotargeted analysis using a segment data dependent acquisition strategy by LC-MS/MS for a metabolomics study of liquiritin in the treatment of depression. J Sep Sci. 2020 Mar 07;: Authors: Yang J, Jin W, Liu D, Zhong Q, Zhou T Abstract An enhanced pseudotargeted method using a segment data dependent acquisition mode based on ultra-high performance liquid chromatography-mass spectrometry was developed. This segment data dependent acquisition-based pseudotargeted method could improve the detection of co-eluted ions and extend the coverage of analytes. A set of 502 multiple reaction monitoring channels were obtained by this segment strategy, which was twice the number created by the traditional data dependent acquisition mode. Compared with the untargeted method, the pseudotargeted profiling demonstrated higher sensitivity and higher precision. More than 90% of the metabolites detected by the enhanced pseudotargeted method had RSDs less than 15%. The segment data dependent acquisition-based pseudotargeted method was successfully applied to the metabolomics study of the depressed rats with the treatment of liquiritin. Forty-seven differential metabolites were screened and five metabolic pathways were found to be relate to depression including retinol metabolism, phenylalanine, tyrosine and tryptophan biosynthesis, phenylalanine metabolism, terpenoid backbone biosynthesis and lysine degradation. The segment data dependent acquisition-based pseudotargeted method widened the coverage of metabolites with good sensitivity and precision, which exhibited great potential in the discovery of differential metabolites in metabolomics studies. This article is protected by copyright. All rights reserved. PMID: 32144949 [PubMed - as supplied by publisher]

Related Articles Current status of retention time prediction in metabolite identification. J Sep Sci. 2020 Mar 07;: Authors: Witting M, Böcker S Abstract Metabolite identification is a crucial step in non-targeted metabolomics, but also represents one of its current bottlenecks. Accurate identifications are required for correct biological interpretation. To date, annotation and identification are usually based on the use of accurate mass search or tandem MS analysis, but neglect orthogonal information such as retention times obtained by chromatographic separation. While several tools are available for the analysis and prediction of tandem MS data, prediction of retention times for metabolite identification are not widespread. Here, we review the current state of retention time prediction in liquid chromatography-mass spectrometry-based metabolomics, with a focus on publications published after 2010. This article is protected by copyright. All rights reserved. PMID: 32144942 [PubMed - as supplied by publisher]

Related Articles A role for metabolomics in the anti-doping toolbox? Drug Test Anal. 2020 Mar 06;: Authors: Narduzzi L, Dervilly G, Audran M, Le Bizec B, Buisson C Abstract The evidence of continuous rise of novel doping agents and novel doping strategies calls for the development of more accurate multi-target screening methods. Direct multi-target screening approaches are restricted to the targeted substances and their turnover. The development of effective "indirect" screening methods requires a priori deep-understanding of the substance metabolism. The biological passport has been demonstrated to be very effective, but it is limited to about 20 indirect parameters. The standard anti-doping analytical methods are hence targeted and does not aim to directly identify unknown substances. Also, the detection of doping agents is limited by the substances excretion. We propose to consider metabolomics for screening of abuse of performance enhancing hormones by athletes, with the basis on the following pieces of evidence: 1) Hormones have a strong influence on human metabolism, changing several parameters in many tissues, organs, and bio-fluids. 2) Metabolomics has been demonstrated to be a very accurate tool to depict the metabolic status of several organisms, tissues and for several human diseases, including hormonal deficiencies. 3) Metabolomics has been demonstrated to be able to distinguish hormone-treated animals from controls in many species, without the need of a priori knowledge of the metabolism for the specific substance. The literature shows that metabolomics could be an appropriate tool to detect hormonal abuse, keeping in mind the strength and the limitation of such an approach. PMID: 32144900 [PubMed - as supplied by publisher]