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Network analysis of plasma and tissue amino acids and the generation of an amino index for potential diagnostic use

Noguchi, Yasushi, Zhang, Qing-Wei, Sugimoto, Tetsuya, Furuhata, Yasufumi, Sakai, Ryosei, Mori, Masato, Takahashi, Mitsuo, Kimura, Takeshi
American journal of clinical nutrition 2006 v.83 no.2 pp. 513S-519S
metabolomics, metabolome, rats, diabetes, dietary protein, protein intake, amino acid metabolism, biochemical pathways, free amino acids, animal organs, blood serum, signal peptide, protein transport, disease diagnosis, diagnostic techniques, algorithms
BACKGROUND: Few studies exist on the use of metabolic profiling of amino acids to examine underlying physiologic and disease states. OBJECTIVE: We aimed to introduce a new method for studying relations among amino acids and to generate a diagnostic index, or amino index, based on amino acid concentrations. DESIGN: For network analysis, 35 Fischer-344 rats were randomly divided into 7 groups and fed diets containing 5%, 10%, 15%, 20%, 30%, 50%, or 70% protein. Amino acid concentrations in plasma and various organs were used to derive correlation coefficients that were then used to construct correlation networks. To build a diagnostic index for diabetic rats, the plasma amino acid concentrations of diabetic and normal rats were analyzed by using a novel algorithm developed to generate amino acid-based indexes. Plasma amino acid concentrations from human growth hormone transgenic rats and insulin-treated diabetic rats were used to evaluate the index obtained for diabetes. Dimethylnitrosamine-treated Sprague-Dawley rats were used to generate an index for hepatic fibrosis. RESULTS: The scatter plots of plasma amino acid concentrations showed distinct patterns in different organs that were due to the different protein contents of the diets. Network analysis showed that data-driven networks for blood and tissue could be obtained. We derived a diagnostic index for the discrimination of diabetic rats with both sensitivity and specificity >97% and another surrogate index for liver hydroxyproline with a correlation of r² = 0.85. CONCLUSIONS: Correlation-based network analysis may help to uncover specific physiologic conditions or states. A novel approach using amino acid molar ratios was shown to generate indexes that can be used to separate animal disease models and monitor the progression of a disease parameter. Some of the methods described here may be applicable to the clinical setting.