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Interactive network analysis of the plasma amino acids profile in a mouse model of hyperglycemia

Takayuki Tanaka14*, Taiga Mochida2, Yukihiro Maki3, Yasuko Shiraki2, Hiroko Mori2, Shirou Matsumoto2, Kazutaka Shimbo4, Toshihiko Ando5, Kimitoshi Nakamura2, Fumio Endo2 and Masahiro Okamoto1

Author Affiliations

1 Innovative Science and Technology for Bio-industry, Graduate School of Bioresource and Bioenvironmental Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582, Japan

2 Department of Pediatrics, Graduate School of Medical Sciences, Kumamoto University, 1-1-1 Honjo, Kumamoto, Kumamoto 860-8556, Japan

3 Department of Digital Media, Fukuoka International University, 4-16-1 Gojo, Dazaifu-city, Fukuoka 818-0193, Japan

4 Institute for Innovation, Ajinomoto Co., Inc., 1-1 Suzuki-cho, Kawasakiku, Kawasaki 210-8681, Japan

5 AminoIndex Department, AJINOMOTO Co., Inc., 15-1, Kyobashi 1-Chome, Chuo-ku, Tokyo 104-8315, Japan

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SpringerPlus 2013, 2:287  doi:10.1186/2193-1801-2-287

Published: 28 June 2013


Amino acids are a group of metabolites that are important substrates for protein synthesis, are important as signaling molecules and play central roles as highly connected metabolic hubs, and therefore, there are many reports that describe disease-specific abnormalities in plasma amino acids profile. However, the causes of progression from a healthy control to a manifestation of the plasma amino acid changes remain obscure. Here, we extended the plasma amino acids profile to relationships that have interactive properties, and found remarkable differences in the longitudinal transition of hyperglycemia as a diabetes emergency. What is especially important is to understand pathogenesis for better treatment and early diagnosis of diabetes. In this study, we performed interactive analysis using time course data of the plasma samples of AKITA mice, which develop hyperglycemia. Primarily, we decided to analyze the interactive property of amino acids which had highly significant association with hyperglycemia, namely alanine, glycine, leucine, isoleucine and valine. Next, we inferred the interactive network structure, which reproduces the actual time course within an error allowance of 10% using an S-system model (a conceptual mathematical model for analyzing and simulating networks). The emphasis of this study was altered interactions of plasma amino acids that show stabilizing and destabilizing features in a variety of clinical settings. By performing sensitivity analysis, the most dominant relations in this network were selected; the control paths from glycine to isoleucine in healthy control and from alanine to glycine in hyperglycemia. This result is in good agreement with the biological knowledge regarding branched-chain amino acids, and suggests the biological importance of the effect from alanine to glycine.

Plasma amino acids; Hyperglycemia; Time course data; Interactive network analysis