The Human Urine Metabolome
Souhaila Bouatra,Farid Aziat,Rupasri Mandal,An Chi Guo,Michael Wilson,Craig Knox,Trent C. Bjorndahl,Ramanarayan Krishnamurthy,Fozia Saleem,Philip B. Liu,Zerihun T. Dame,Jenna Poelzer,Jessica Huynh,Faizath S. Yallou,Nick Psychogios,Edison Dong,Ralf Bogumil,Cornelia Roehring,David S. Wishart,David S. Wishart +19 more
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TLDR
A comprehensive, quantitative, metabolome-wide characterization of human urine and the identification and annotation of several previously unknown urine metabolites and to substantially enhance the level of metabolome coverage are undertaken.Abstract:
Urine has long been a “favored” biofluid among metabolomics researchers. It is sterile, easy-to-obtain in large volumes, largely free from interfering proteins or lipids and chemically complex. However, this chemical complexity has also made urine a particularly difficult substrate to fully understand. As a biological waste material, urine typically contains metabolic breakdown products from a wide range of foods, drinks, drugs, environmental contaminants, endogenous waste metabolites and bacterial by-products. Many of these compounds are poorly characterized and poorly understood. In an effort to improve our understanding of this biofluid we have undertaken a comprehensive, quantitative, metabolome-wide characterization of human urine. This involved both computer-aided literature mining and comprehensive, quantitative experimental assessment/validation. The experimental portion employed NMR spectroscopy, gas chromatography mass spectrometry (GC-MS), direct flow injection mass spectrometry (DFI/LC-MS/MS), inductively coupled plasma mass spectrometry (ICP-MS) and high performance liquid chromatography (HPLC) experiments performed on multiple human urine samples. This multi-platform metabolomic analysis allowed us to identify 445 and quantify 378 unique urine metabolites or metabolite species. The different analytical platforms were able to identify (quantify) a total of: 209 (209) by NMR, 179 (85) by GC-MS, 127 (127) by DFI/LC-MS/MS, 40 (40) by ICP-MS and 10 (10) by HPLC. Our use of multiple metabolomics platforms and technologies allowed us to identify several previously unknown urine metabolites and to substantially enhance the level of metabolome coverage. It also allowed us to critically assess the relative strengths and weaknesses of different platforms or technologies. The literature review led to the identification and annotation of another 2206 urinary compounds and was used to help guide the subsequent experimental studies. An online database containing the complete set of 2651 confirmed human urine metabolite species, their structures (3079 in total), concentrations, related literature references and links to their known disease associations are freely available at http://www.urinemetabolome.ca.read more
Citations
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HMDB 4.0: the human metabolome database for 2018.
David S. Wishart,Yannick Djoumbou Feunang,Ana Marcu,An Chi Guo,Kevin Y. H. Liang,Rosa Vázquez-Fresno,Tanvir Sajed,Daniel Johnson,Carin Li,Naama Karu,Zinat Sayeeda,Elvis J. Lo,Nazanin Assempour,Mark V. Berjanskii,Sandeep Singhal,David Arndt,Yongjie Liang,Hasan Badran,Jason R. Grant,Arnau Serra-Cayuela,Yifeng Liu,Rupa Mandal,Vanessa Neveu,Allison Pon,Craig Knox,Michael Wilson,Claudine Manach,Augustin Scalbert +27 more
TL;DR: This year's update to the HMDB, HMDB 4.0, represents the most significant upgrade to the database in its history and should greatly enhance its ease of use and its potential applications in nutrition, biochemistry, clinical chemistry, clinical genetics, medicine, and metabolomics science.
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Emerging applications of metabolomics in drug discovery and precision medicine
TL;DR: This Review discusses some of the latest technological advances in metabolomics, focusing on the application of metabolomics towards uncovering the underlying causes of complex diseases, the growing role of metabolites in drug discovery and its potential effect on precision medicine.
Journal ArticleDOI
Analysis of the Human Adult Urinary Metabolome Variations with Age, Body Mass Index, and Gender by Implementing a Comprehensive Workflow for Univariate and OPLS Statistical Analyses
TL;DR: The impact of gender and age on the urinary metabolome is highlighted, and thus it indicates that these factors should be taken into account for the design of metabolomics studies.
Journal ArticleDOI
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Chattopadhyay,Sue Chow,Eleni Christakou,Eleni Christakou,Luka Cicin-Sain,Mario Clerici,Federico Colombo,Laura Cook,Anne Cooke,Andrea M. Cooper,Alexandra J. Corbett,Antonio Cosma,Lorenzo Cosmi,Pierre Coulie,Ana Cumano,Ljiljana Cvetkovic,Van Duc Dang,Chantip Dang-Heine,Martin S. Davey,Derek Davies,Sara De Biasi,Genny Del Zotto,Gelo Victoriano Dela Cruz,Michael Delacher,Silvia Della Bella,Paolo Dellabona,Günnur Deniz,Mark C. Dessing,James P. Di Santo,Andreas Diefenbach,Francesco Dieli,Andreas Dolf,Thomas Dörner,Regine J. Dress,Diana Dudziak,Michael L. Dustin,Charles-Antoine Dutertre,Charles-Antoine Dutertre,Friederike Ebner,Sidonia B G Eckle,Matthias Edinger,Pascale Eede,Götz R. A. Ehrhardt,Marcus Eich,Pablo Engel,Britta Engelhardt,Anna Erdei,Charlotte Esser,Bart Everts,Maximilien Evrard,Christine S. Falk,Todd A. Fehniger,Mar Felipo-Benavent,Helen Ferry,Markus Feuerer,Andrew Filby,Kata Filkor,Simon Fillatreau,Marie Follo,Irmgard Förster,John Bellamy Foster,Gemma A. 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TL;DR: These guidelines are a consensus work of a considerable number of members of the immunology and flow cytometry community providing the theory and key practical aspects offlow cytometry enabling immunologists to avoid the common errors that often undermine immunological data.
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TL;DR: This review will highlight a number of emerging NMR techniques and technologies that are being used to strengthen its utility and overcome its inherent limitations in metabolomic applications.
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