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

Author:
Noguchi, Yasushi, Zhang, Qing-Wei, Sugimoto, Tetsuya, Furuhata, Yasufumi, Sakai, Ryosei, Mori, Masato, Takahashi, Mitsuo, Kimura, Takeshi
Source:
American journal of clinical nutrition 2006 v.83 no.2 pp. 513S-519S
ISSN:
0002-9165
Subject:
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
Abstract:
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.
Agid:
680919