Literature DB >> 11373073

Metabolic profiling of genetic disorders: a multitissue (1)H nuclear magnetic resonance spectroscopic and pattern recognition study into dystrophic tissue.

J L Griffin1, H J Williams, E Sang, K Clarke, C Rae, J K Nicholson.   

Abstract

A principal problem in understanding the functional genomics of a pathology is the wide-reaching biochemical effects that occur when the expression of a given protein is altered. To complement the information available to bioinformatics through genomic and proteomic approaches, a novel method of providing metabolite profiles for a disease is suggested, using pattern recognition coupled with (1)H NMR spectroscopy. Using this technique the mdx mouse, a model of Duchenne muscular dystrophy (DMD) was examined. Dystrophic tissue had distinct metabolic profiles not only for cardiac and other muscle tissues, but also in the cerebral cortex and cerebellum, where the role of dystrophin is still controversial. These metabolic ratios were expressed crudely as biomarker ratios to demonstrate the effectiveness of the approach at separating dystrophic from control tissue (cardiac (taurine/creatine): mdx = 2.08 +/- 0.04, control 1.55 +/- 0.04, P < 0.005; cortex (phosphocholine/taurine): mdx = 1.28 +/- 0.12, control = 0.83 +/- 0.05, P < 0.01; cerebellum (glutamate/creatine): mdx = 0.49 +/- 0.03, control = 0.34 +/- 0.03, P < 0.01). This technique produced new metabolic biomarkers for following disease progression but also demonstrated that many metabolic pathways are perturbed in dystrophic tissue. Copyright 2001 Academic Press.

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Mesh:

Year:  2001        PMID: 11373073     DOI: 10.1006/abio.2001.5096

Source DB:  PubMed          Journal:  Anal Biochem        ISSN: 0003-2697            Impact factor:   3.365


  24 in total

Review 1.  Metabolic profiles to define the genome: can we hear the phenotypes?

Authors:  Julian L Griffin
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2004-06-29       Impact factor: 6.237

Review 2.  Metabolomics as a tool for cardiac research.

Authors:  Julian L Griffin; Helen Atherton; John Shockcor; Luigi Atzori
Journal:  Nat Rev Cardiol       Date:  2011-09-20       Impact factor: 32.419

3.  Metabolomics and machine learning: explanatory analysis of complex metabolome data using genetic programming to produce simple, robust rules.

Authors:  Douglas B Kell
Journal:  Mol Biol Rep       Date:  2002       Impact factor: 2.316

Review 4.  Biomarkers for neuroAIDS: the widening scope of metabolomics.

Authors:  Gurudutt Pendyala; Elizabeth J Want; William Webb; Gary Siuzdak; Howard S Fox
Journal:  J Neuroimmune Pharmacol       Date:  2006-10-10       Impact factor: 4.147

Review 5.  The Cinderella story of metabolic profiling: does metabolomics get to go to the functional genomics ball?

Authors:  Julian L Griffin
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2006-01-29       Impact factor: 6.237

6.  Increasing taurine intake and taurine synthesis improves skeletal muscle function in the mdx mouse model for Duchenne muscular dystrophy.

Authors:  Jessica R Terrill; Gavin J Pinniger; Jamie A Graves; Miranda D Grounds; Peter G Arthur
Journal:  J Physiol       Date:  2016-01-18       Impact factor: 5.182

7.  Dysregulation of Intracellular Ca2+ in Dystrophic Cortical and Hippocampal Neurons.

Authors:  José R Lopez; Juan Kolster; Arkady Uryash; Eric Estève; Francisco Altamirano; José A Adams
Journal:  Mol Neurobiol       Date:  2016-12-15       Impact factor: 5.590

8.  Longitudinal metabolomic analysis of plasma enables modeling disease progression in Duchenne muscular dystrophy mouse models.

Authors:  Roula Tsonaka; Mirko Signorelli; Ekrem Sabir; Alexandre Seyer; Kristina Hettne; Annemieke Aartsma-Rus; Pietro Spitali
Journal:  Hum Mol Genet       Date:  2020-03-27       Impact factor: 6.150

9.  Age-dependent changes in metabolite profile and lipid saturation in dystrophic mice.

Authors:  Brittany Lee-McMullen; Stephen M Chrzanowski; Ravneet Vohra; Sean C Forbes; Krista Vandenborne; Arthur S Edison; Glenn A Walter
Journal:  NMR Biomed       Date:  2019-03-08       Impact factor: 4.044

10.  A metabonomic approach to analyze the dexamethasone-induced cleft palate in mice.

Authors:  Jinglin Zhou; Bin Xu; Bing Shi; Jing Huang; Wei He; Shengjun Lu; Junjun Lu; Liying Xiao; Wei Li
Journal:  J Biomed Biotechnol       Date:  2010-08-10
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