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


METHODS
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.
Research Institute for Health Fundamentals, Ajinomoto Company Inc, Kanagawa, Japan.
Yasushi
Y
Noguchi
Qing-Wei
QW
Zhang
Tetsuya
T
Sugimoto
Yasufumi
Y
Furuhata
Ryosei
R
Sakai
Masato
M
Mori
Mitsuo
M
Takahashi
Takeshi
T
Kimura
D000465:Algorithms; D000596:Amino Acids / Q000097:blood / Q000378:metabolism*; D000818:Animals; D015415:Biomarkers / Q000097:blood; D016000:Cluster Analysis; D003921:Diabetes Mellitus, Experimental / Q000175:diagnosis / Q000378:metabolism*; D004044:Dietary Proteins / Q000008:administration & dosage* / Q000378:metabolism; D004195:Disease Models, Animal; D004305:Dose-Response Relationship, Drug; D008103:Liver Cirrhosis / Q000175:diagnosis / Q000378:metabolism*; D008297:Male; D009928:Organ Specificity; D011897:Random Allocation; D051381:Rats; D011916:Rats, Inbred F344; D017207:Rats, Sprague-Dawley; D012680:Sensitivity and Specificity
D000596:Amino Acids; D015415:Biomarkers; D004044:Dietary Proteins