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      Establishment of Protocols for Global Metabolomics by LC-MS for Biomarker Discovery

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          Abstract

          Metabolomics is a promising avenue for biomarker discovery. Although the quality of metabolomic analyses, especially global metabolomics (G-Met) using mass spectrometry (MS), largely depends on the instrumentation, potential bottlenecks still exist at several basic levels in the metabolomics workflow. Therefore, we established a precise protocol initially for the G-Met analyses of human blood plasma to overcome some these difficulties. In our protocol, samples are deproteinized in a 96-well plate using an automated liquid-handling system, and conducted either using a UHPLC-QTOF/MS system equipped with a reverse phase column or a LC-FTMS system equipped with a normal phase column. A normalization protocol of G-Met data was also developed to compensate for intra- and inter-batch differences, and the variations were significantly reduced along with our normalization, especially for the UHPLC-QTOF/MS data with a C18 reverse-phase column for positive ions. Secondly, we examined the changes in metabolomic profiles caused by the storage of EDTA-blood specimens to identify quality markers for the evaluation of the specimens’ pre-analytical conditions. Forty quality markers, including lysophospholipids, dipeptides, fatty acids, succinic acid, amino acids, glucose, and uric acid were identified by G-Met for the evaluation of plasma sample quality and established the equation of calculating the quality score. We applied our quality markers to a small-scale study to evaluate the quality of clinical samples. The G-Met protocols and quality markers established here should prove useful for the discovery and development of biomarkers for a wider range of diseases.

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          Human metabolic phenotype diversity and its association with diet and blood pressure.

          Metabolic phenotypes are the products of interactions among a variety of factors-dietary, other lifestyle/environmental, gut microbial and genetic. We use a large-scale exploratory analytical approach to investigate metabolic phenotype variation across and within four human populations, based on 1H NMR spectroscopy. Metabolites discriminating across populations are then linked to data for individuals on blood pressure, a major risk factor for coronary heart disease and stroke (leading causes of mortality worldwide). We analyse spectra from two 24-hour urine specimens for each of 4,630 participants from the INTERMAP epidemiological study, involving 17 population samples aged 40-59 in China, Japan, UK and USA. We show that urinary metabolite excretion patterns for East Asian and western population samples, with contrasting diets, diet-related major risk factors, and coronary heart disease/stroke rates, are significantly differentiated (P < 10(-16)), as are Chinese/Japanese metabolic phenotypes, and subgroups with differences in dietary vegetable/animal protein and blood pressure. Among discriminatory metabolites, we quantify four and show association (P < 0.05 to P < 0.0001) of mean 24-hour urinary formate excretion with blood pressure in multiple regression analyses for individuals. Mean 24-hour urinary excretion of alanine (direct) and hippurate (inverse), reflecting diet and gut microbial activities, are also associated with blood pressure of individuals. Metabolic phenotyping applied to high-quality epidemiological data offers the potential to develop an area of aetiopathogenetic knowledge involving discovery of novel biomarkers related to cardiovascular disease risk.
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            Global metabolic profiling procedures for urine using UPLC-MS.

            The production of 'global' metabolite profiles involves measuring low molecular-weight metabolites (<1 kDa) in complex biofluids/tissues to study perturbations in response to physiological challenges, toxic insults or disease processes. Information-rich analytical platforms, such as mass spectrometry (MS), are needed. Here we describe the application of ultra-performance liquid chromatography-MS (UPLC-MS) to urinary metabolite profiling, including sample preparation, stability/storage and the selection of chromatographic conditions that balance metabolome coverage, chromatographic resolution and throughput. We discuss quality control and metabolite identification, as well as provide details of multivariate data analysis approaches for analyzing such MS data. Using this protocol, the analysis of a sample set in 96-well plate format, would take ca. 30 h, including 1 h for system setup, 1-2 h for sample preparation, 24 h for UPLC-MS analysis and 1-2 h for initial data processing. The use of UPLC-MS for metabolic profiling in this way is not faster than the conventional HPLC-based methods but, because of improved chromatographic performance, provides superior metabolome coverage.
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              Global metabolic profiling of animal and human tissues via UPLC-MS.

              Obtaining comprehensive, untargeted metabolic profiles for complex solid samples, e.g., animal tissues, requires sample preparation and access to information-rich analytical methodologies such as mass spectrometry (MS). Here we describe a practical two-step process for tissue samples that is based on extraction into 'aqueous' and 'organic' phases for polar and nonpolar metabolites. Separation methods such as ultraperformance liquid chromatography (UPLC) in combination with MS are needed to obtain sufficient resolution to create diagnostic metabolic profiles and identify candidate biomarkers. We provide detailed protocols for sample preparation, chromatographic procedures, multivariate analysis and metabolite identification via tandem MS (MS/MS) techniques and high-resolution MS. By using these optimized approaches, analysis of a set of samples using a 96-well plate format would take ~48 h: 1 h for system setup, 8-10 h for sample preparation, 34 h for UPLC-MS analysis and 2-3 h for preliminary/exploratory data processing, representing a robust method for untargeted metabolic screening of tissue samples.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                31 August 2016
                2016
                : 11
                : 8
                : e0160555
                Affiliations
                [1 ]Department of Integrative Genomics, Tohoku Medical Megabank Organization, Tohoku University, Sendai, Miyagi, Japan
                [2 ]Medical Biochemistry, Tohoku University School of Medicine, Sendai, Miyagi, Japan
                [3 ]CREST, Japan Agency for Medical Research and Development (AMED), Chiyoda, Tokyo, Japan
                [4 ]Department of Systems Bioinformatics, Graduate School of Information Sciences, Tohoku University, Sendai, Miyagi, Japan
                [5 ]Department of Biochemistry, Tohoku University Graduate School of Medicine, Sendai, Miyagi, Japan
                [6 ]Department of Gynecology and Obstetrics, Tohoku University Graduate School of Medicine, Sendai, Miyagi, Japan
                [7 ]Department of Gene Expression Regulation, Institute of Development, Aging and Cancer, Tohoku University, Sendai, Miyagi, Japan
                Instituto de Investigacion Sanitaria INCLIVA, SPAIN
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                • Conceptualization: DS JS SK JY HM OT KK MY.

                • Data curation: DS YO INM KK.

                • Formal analysis: DS OT.

                • Funding acquisition: DS YO OT KK MY.

                • Investigation: DS Y. Kurosawa Y. Katoh.

                • Methodology: DS Y. Katoh Y. Kurosawa RS.

                • Project administration: DS JY OT KK MY.

                • Resources: DS SK OT KK MY.

                • Software: DS YO INM KK.

                • Supervision: SK JY OT KK MY.

                • Validation: DS Y. Katoh.

                • Visualization: DS YO INM KK.

                • Writing - original draft: DS.

                • Writing - review & editing: DS YO OT KK MY.

                Author information
                http://orcid.org/0000-0001-9484-9870
                Article
                PONE-D-16-06339
                10.1371/journal.pone.0160555
                5006994
                27579980
                3a0c51b5-928a-40f9-b64f-03070182938b
                © 2016 Saigusa et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 14 February 2016
                : 20 July 2016
                Page count
                Figures: 4, Tables: 1, Pages: 18
                Funding
                Funded by: The Mochida Memorial Foundation for Medical and Pharmaceutical Research
                Award Recipient :
                Funded by: Kanzawa Medical Research Foundation
                Award Recipient :
                Funded by: CREST, AMED
                Award ID: J160000701
                Funded by: Tohoku Medical Megabank Project (Special Account for Reconstruction from the Great East Japan Earthquake)
                This investigation was also supported in parts by 2015 The Mochida Memorial Foundation for Medical and Pharmaceutical Research ( http://www.mochida.co.jp/zaidan/index.html), 2015 Kanzawa Medical Research Foundation ( http://www.kissei.co.jp/fund/fund.htm), and CREST, AMED (J160000701). This work was partially supported by the Tohoku Medical Megabank Project (Special Account for Reconstruction from the Great East Japan Earthquake). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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