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      Metabolic profiling of presymptomatic Huntington’s disease sheep reveals novel biomarkers

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          Abstract

          The pronounced cachexia (unexplained wasting) seen in Huntington’s disease (HD) patients suggests that metabolic dysregulation plays a role in HD pathogenesis, although evidence of metabolic abnormalities in HD patients is inconsistent. We performed metabolic profiling of plasma from presymptomatic HD transgenic and control sheep. Metabolites were quantified in sequential plasma samples taken over a 25 h period using a targeted LC/MS metabolomics approach. Significant changes with respect to genotype were observed in 89/130 identified metabolites, including sphingolipids, biogenic amines, amino acids and urea. Citrulline and arginine increased significantly in HD compared to control sheep. Ten other amino acids decreased in presymptomatic HD sheep, including branched chain amino acids (isoleucine, leucine and valine) that have been identified previously as potential biomarkers of HD. Significant increases in urea, arginine, citrulline, asymmetric and symmetric dimethylarginine, alongside decreases in sphingolipids, indicate that both the urea cycle and nitric oxide pathways are dysregulated at early stages in HD. Logistic prediction modelling identified a set of 8 biomarkers that can identify 80% of the presymptomatic HD sheep as transgenic, with 90% confidence. This level of sensitivity, using minimally invasive methods, offers novel opportunities for monitoring disease progression in HD patients.

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          Most cited references 68

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          MetaboAnalyst 3.0—making metabolomics more meaningful

          MetaboAnalyst (www.metaboanalyst.ca) is a web server designed to permit comprehensive metabolomic data analysis, visualization and interpretation. It supports a wide range of complex statistical calculations and high quality graphical rendering functions that require significant computational resources. First introduced in 2009, MetaboAnalyst has experienced more than a 50X growth in user traffic (>50 000 jobs processed each month). In order to keep up with the rapidly increasing computational demands and a growing number of requests to support translational and systems biology applications, we performed a substantial rewrite and major feature upgrade of the server. The result is MetaboAnalyst 3.0. By completely re-implementing the MetaboAnalyst suite using the latest web framework technologies, we have been able substantially improve its performance, capacity and user interactivity. Three new modules have also been added including: (i) a module for biomarker analysis based on the calculation of receiver operating characteristic curves; (ii) a module for sample size estimation and power analysis for improved planning of metabolomics studies and (iii) a module to support integrative pathway analysis for both genes and metabolites. In addition, popular features found in existing modules have been significantly enhanced by upgrading the graphical output, expanding the compound libraries and by adding support for more diverse organisms.
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            Statistical total correlation spectroscopy: an exploratory approach for latent biomarker identification from metabolic 1H NMR data sets.

            We describe here the implementation of the statistical total correlation spectroscopy (STOCSY) analysis method for aiding the identification of potential biomarker molecules in metabonomic studies based on NMR spectroscopic data. STOCSY takes advantage of the multicollinearity of the intensity variables in a set of spectra (in this case 1H NMR spectra) to generate a pseudo-two-dimensional NMR spectrum that displays the correlation among the intensities of the various peaks across the whole sample. This method is not limited to the usual connectivities that are deducible from more standard two-dimensional NMR spectroscopic methods, such as TOCSY. Moreover, two or more molecules involved in the same pathway can also present high intermolecular correlations because of biological covariance or can even be anticorrelated. This combination of STOCSY with supervised pattern recognition and particularly orthogonal projection on latent structure-discriminant analysis (O-PLS-DA) offers a new powerful framework for analysis of metabonomic data. In a first step O-PLS-DA extracts the part of NMR spectra related to discrimination. This information is then cross-combined with the STOCSY results to help identify the molecules responsible for the metabolic variation. To illustrate the applicability of the method, it has been applied to 1H NMR spectra of urine from a metabonomic study of a model of insulin resistance based on the administration of a carbohydrate diet to three different mice strains (C57BL/6Oxjr, BALB/cOxjr, and 129S6/SvEvOxjr) in which a series of metabolites of biological importance can be conclusively assigned and identified by use of the STOCSY approach.
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              Metabolic phenotyping in health and disease.

              Analyzing metabolites (small molecules <1 kDa) in body fluids such as urine and plasma using various spectroscopic methods provides information on the metabotype (metabolic phenotype) of individuals or populations, information that can be applied to personalized medicine or public healthcare.
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                Author and article information

                Affiliations
                [1 ]Chronobiology, Faculty of Health and Medical Sciences, University of Surrey , Guildford GU2 7XH, United Kingdom
                [2 ]Preclinical, Imaging & Research Laboratories (PIRL), SAHMRI , Gilles Plains, Adelaide, Australia
                [3 ]National Institute for Public Health and the Environment (RIVM) , 3720 BA Bilthoven, The Netherlands
                [4 ]South Australian Research and Development Institute , Roseworthy, South Australia
                [5 ]Department of Physiology, Development and Neuroscience, University of Cambridge , Downing Street, Cambridge CB2 3DY, United Kingdom
                Author notes
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group
                2045-2322
                22 February 2017
                2017
                : 7
                28223686 5320451 srep43030 10.1038/srep43030
                Copyright © 2017, The Author(s)

                This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

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