Publications on metabolic markers relating to genetic strain differences and disorders in rodents have been reported [11, 52], and these applications will increase rapidly in the next decade, particularly with the integration of genomic, proteomic, and metabonomics techniques. 1H NMR and pattern recognition was used to examine the mdx mouse as a model of the muscle-wasting disease Duchenne muscular dystrophy (DMD) in which the muscle protein dystrophin is not expressed. Dystrophic mouse brain and cardiac tissue extracts showed distinct metabolic profiles with altered ratios of creatine, taurine, and choline-containing metabolites. It was suggested that these ratios could be used to correlate cerebral deficits with metabolic abnormalities in DMD sufferers by using NMR in vivo .
Scott and collaborators at Imperial College London have also been concerned with gene discovery using both animal and human models of the metabolic syndrome, which is the constellation of disorders related to insulin resistance and includes obesity, dyslipidaemia, diabetes mellitus, hypertension, and increased risk of atherosclerosis. A biological atlas of insulin resistance (BAIR) is currently under development using genetically engineered and environmental models of insulin resistance along with multimodality phenotyping . This approach aims to integrate tran-scriptomics, phospho- and glycoproteomics, metabonomics, and structural biology to advance new hypothesis-driven research toward better understanding and treatment of metabolic syndrome.
Another clinically focused metabolic project that relies heavily on flow-injection NMR is the metabonomics and genomics in coronary artery disease (MAGICAD) project . In a pilot study involving the 1H NMR-PR analysis of serum samples from 80 patients, half of whom had severe coronary artery disease (CAD), >90% of the subjects with severe CAD were correctly diagnosed by the NMR-PR approach . The BAIR and MAGICAD projects are amongst the first clinical projects to attempt to integrate the genomic, proteomic, and metabolic-profiling technologies, and these approaches may be extended to diagnostics in toxicogenomics.
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