13 research outputs found

    Development of Isotope Labeling LC–MS for Human Salivary Metabolomics and Application to Profiling Metabolome Changes Associated with Mild Cognitive Impairment

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    Saliva is a readily available biofluid that may contain metabolites of interest for diagnosis and prognosis of diseases. In this work, a differential <sup>13</sup>C/<sup>12</sup>C isotope dansylation labeling method, combined with liquid chromatography Fourier transform ion cyclotron resonance mass spectrometry (LC–FTICR-MS), is described for quantitative profiling of the human salivary metabolome. New strategies are presented to optimize the sample preparation and LC–MS detection processes. The strategies allow the use of as little of 5 μL of saliva sample as a starting material to determine the concentration changes of an average of 1058 ion pairs or putative metabolites in comparative saliva samples. The overall workflow consists of several steps including acetone-induced protein precipitation, <sup>12</sup>C-dansylation labeling of the metabolites, and LC–UV measurement of the total concentration of the labeled metabolites in individual saliva samples. A pooled sample was prepared from all the individual samples and labeled with <sup>13</sup>C-dansylation to serve as a reference. Using this metabolome profiling method, it was found that compatible metabolome results could be obtained after saliva samples were stored in tubes normally used for genetic material collection at room temperature, −20 °C freezer, and −80 °C freezer over a period of 1 month, suggesting that many saliva samples already collected in genomic studies could become a valuable resource for metabolomics studies, although the effect of much longer term of storage remains to be determined. Finally, the developed method was applied for analyzing the metabolome changes of two different groups: normal healthy older adults and comparable older adults with mild cognitive impairment (MCI). Top-ranked 18 metabolites successfully distinguished the two groups, among which seven metabolites were putatively identified while one metabolite, taurine, was definitively identified

    Additional file 1 of Effect of an increase in Lp(a) following statin therapy on cardiovascular prognosis in secondary prevention population of coronary artery disease

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    Additional file 1: Table S1. Statins used in study subjects. Table S2. Endpoint events for study subjects. Table S3. Univariate COX analysis of risk factors for MACE

    Effective Antisense Gene Regulation via Noncationic, Polyethylene Glycol Brushes

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    Negatively charged nucleic acids are often complexed with polycationic transfection agents before delivery. Herein, we demonstrate that a noncationic, biocompatible polymer, polyethylene glycol, can be used as a transfection vector by forming a brush polymer-DNA conjugate. The brush architecture provides embedded DNA strands with enhanced nuclease stability and improved cell uptake. Because of the biologically benign nature of the polymer component, no cytotoxicity was observed. This approach has the potential to address several long-lasting challenges in oligonucleotide therapeutics

    Table1_Impact of epicardial adipose tissue volume on hemodynamically significant coronary artery disease in Chinese patients with known or suspected coronary artery disease.docx

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    BackgroundEpicardial adipose tissue (EAT) is directly related to coronary artery disease (CAD), but little is known about its role in hemodynamically significant CAD. Therefore, our goal is to explore the impact of EAT volume on hemodynamically significant CAD.MethodsPatients who underwent coronary computed tomography angiography (CCTA) and received coronary angiography within 30 days were retrospectively included. Measurements of EAT volume and coronary artery calcium score (CACs) were performed on a semi-automatic software based on CCTA images, while quantitative flow ratio (QFR) was automatically calculated by the AngioPlus system according to coronary angiographic images.ResultsThis study included 277 patients, 112 of whom had hemodynamically significant CAD and showed higher EAT volume. In multivariate analysis, EAT volume was independently and positively correlated with hemodynamically significant CAD [per standard deviation (SD) cm3; odds ratio (OR), 2.78; 95% confidence interval (CI), 1.86–4.15; P min (per SD cm3; β coefficient, −0.068; 95% CI, −0.109 to −0.027; P = 0.001) after adjustment for traditional risk factors and CACs. Receiver operating characteristics curve analysis demonstrated a significant improvement in predictive value for hemodynamically significant CAD with the addition of EAT volume to obstructive CAD alone (area under the curve, 0.950 vs. 0.891; P ConclusionIn this study, we found that EAT volume correlated substantially and positively with the existence and severity of hemodynamically significant CAD in Chinese patients with known or suspected CAD, which was independent of traditional risk factors and CACs. In combination with obstructive CAD, EAT volume significantly improved diagnostic performance for hemodynamically significant CAD, suggesting that EAT could be a reliable noninvasive indicator of hemodynamically significant CAD.</p

    MyCompoundID: Using an Evidence-Based Metabolome Library for Metabolite Identification

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    Identification of unknown metabolites is a major challenge in metabolomics. Without the identities of the metabolites, the metabolome data generated from a biological sample cannot be readily linked with the proteomic and genomic information for studies in systems biology and medicine. We have developed a web-based metabolite identification tool (http://www.mycompoundid.org) that allows searching and interpreting mass spectrometry (MS) data against a newly constructed metabolome library composed of 8 021 known human endogenous metabolites and their predicted metabolic products (375 809 compounds from one metabolic reaction and 10 583 901 from two reactions). As an example, in the analysis of a simple extract of human urine or plasma and the whole human urine by liquid chromatography-mass spectrometry and MS/MS, we are able to identify at least two times more metabolites in these samples than by using a standard human metabolome library. In addition, it is shown that the evidence-based metabolome library (EML) provides a much superior performance in identifying putative metabolites from a human urine sample, compared to the use of the ChemPub and KEGG libraries

    MyCompoundID: Using an Evidence-Based Metabolome Library for Metabolite Identification

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    Identification of unknown metabolites is a major challenge in metabolomics. Without the identities of the metabolites, the metabolome data generated from a biological sample cannot be readily linked with the proteomic and genomic information for studies in systems biology and medicine. We have developed a web-based metabolite identification tool (http://www.mycompoundid.org) that allows searching and interpreting mass spectrometry (MS) data against a newly constructed metabolome library composed of 8 021 known human endogenous metabolites and their predicted metabolic products (375 809 compounds from one metabolic reaction and 10 583 901 from two reactions). As an example, in the analysis of a simple extract of human urine or plasma and the whole human urine by liquid chromatography-mass spectrometry and MS/MS, we are able to identify at least two times more metabolites in these samples than by using a standard human metabolome library. In addition, it is shown that the evidence-based metabolome library (EML) provides a much superior performance in identifying putative metabolites from a human urine sample, compared to the use of the ChemPub and KEGG libraries

    MyCompoundID: Using an Evidence-Based Metabolome Library for Metabolite Identification

    No full text
    Identification of unknown metabolites is a major challenge in metabolomics. Without the identities of the metabolites, the metabolome data generated from a biological sample cannot be readily linked with the proteomic and genomic information for studies in systems biology and medicine. We have developed a web-based metabolite identification tool (http://www.mycompoundid.org) that allows searching and interpreting mass spectrometry (MS) data against a newly constructed metabolome library composed of 8 021 known human endogenous metabolites and their predicted metabolic products (375 809 compounds from one metabolic reaction and 10 583 901 from two reactions). As an example, in the analysis of a simple extract of human urine or plasma and the whole human urine by liquid chromatography-mass spectrometry and MS/MS, we are able to identify at least two times more metabolites in these samples than by using a standard human metabolome library. In addition, it is shown that the evidence-based metabolome library (EML) provides a much superior performance in identifying putative metabolites from a human urine sample, compared to the use of the ChemPub and KEGG libraries

    MyCompoundID: Using an Evidence-Based Metabolome Library for Metabolite Identification

    No full text
    Identification of unknown metabolites is a major challenge in metabolomics. Without the identities of the metabolites, the metabolome data generated from a biological sample cannot be readily linked with the proteomic and genomic information for studies in systems biology and medicine. We have developed a web-based metabolite identification tool (http://www.mycompoundid.org) that allows searching and interpreting mass spectrometry (MS) data against a newly constructed metabolome library composed of 8 021 known human endogenous metabolites and their predicted metabolic products (375 809 compounds from one metabolic reaction and 10 583 901 from two reactions). As an example, in the analysis of a simple extract of human urine or plasma and the whole human urine by liquid chromatography-mass spectrometry and MS/MS, we are able to identify at least two times more metabolites in these samples than by using a standard human metabolome library. In addition, it is shown that the evidence-based metabolome library (EML) provides a much superior performance in identifying putative metabolites from a human urine sample, compared to the use of the ChemPub and KEGG libraries

    MyCompoundID: Using an Evidence-Based Metabolome Library for Metabolite Identification

    No full text
    Identification of unknown metabolites is a major challenge in metabolomics. Without the identities of the metabolites, the metabolome data generated from a biological sample cannot be readily linked with the proteomic and genomic information for studies in systems biology and medicine. We have developed a web-based metabolite identification tool (http://www.mycompoundid.org) that allows searching and interpreting mass spectrometry (MS) data against a newly constructed metabolome library composed of 8 021 known human endogenous metabolites and their predicted metabolic products (375 809 compounds from one metabolic reaction and 10 583 901 from two reactions). As an example, in the analysis of a simple extract of human urine or plasma and the whole human urine by liquid chromatography-mass spectrometry and MS/MS, we are able to identify at least two times more metabolites in these samples than by using a standard human metabolome library. In addition, it is shown that the evidence-based metabolome library (EML) provides a much superior performance in identifying putative metabolites from a human urine sample, compared to the use of the ChemPub and KEGG libraries

    MyCompoundID: Using an Evidence-Based Metabolome Library for Metabolite Identification

    No full text
    Identification of unknown metabolites is a major challenge in metabolomics. Without the identities of the metabolites, the metabolome data generated from a biological sample cannot be readily linked with the proteomic and genomic information for studies in systems biology and medicine. We have developed a web-based metabolite identification tool (http://www.mycompoundid.org) that allows searching and interpreting mass spectrometry (MS) data against a newly constructed metabolome library composed of 8 021 known human endogenous metabolites and their predicted metabolic products (375 809 compounds from one metabolic reaction and 10 583 901 from two reactions). As an example, in the analysis of a simple extract of human urine or plasma and the whole human urine by liquid chromatography-mass spectrometry and MS/MS, we are able to identify at least two times more metabolites in these samples than by using a standard human metabolome library. In addition, it is shown that the evidence-based metabolome library (EML) provides a much superior performance in identifying putative metabolites from a human urine sample, compared to the use of the ChemPub and KEGG libraries
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