A novel gene network analysis in liver tissues of diabetic rats in response to resistant starch treatment

Zhiwie Wang, Yinghui Zhang, Runge Shi, Zhongkai Zhou, Fang Wang, Padraig Strappe

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)
6 Downloads (Pure)


In this study, we investigated the genome-wide gene expression profiles in the liver tissue of diabetic rats before and after RS treatment. The microarray-based analysis revealed that a total of 173 genes were up-regulated and 197 genes were down-regulated in response to RS treatment. These genes were mainly related to glucose metabolism (e.g., hexokinase, pyruvate kinase and phosphotransferase etc.), and lipid metabolism (e.g., carnitine palmitoyl transfer 1, fatty acid transporter, beta hydroxyl butyric dehydrogenase etc.). Cluster analysis results showed that the up/down-regulated genes were highly responsive to RS treatment, and were considered to be directly or indirectly associated with reducing plasma glucose and body fat. To interpret the mechanism of RS regulation at the molecular level, a novel gene network was constructed based on 370 up/down-regulated genes coupled with 718 known diabetes-related genes. The topology of the network showed the characteristics of small-world and scale-free network, with some pathways demonstrating a high degree. Forkhead class A signaling pathway, with a degree of 8, was analyzed and was found to have an effect mainly on glucose and lipid metabolism processes. The results indicate that RS can suppress the development of type 2 diabetes in the STZ rat model through modulating the expression of multiple genes involved in glucose and lipid metabolism. The potential application of this novel gene network is also discussed.
Original languageEnglish
Pages (from-to)1-8
Number of pages8
Issue number110
Publication statusPublished - Mar 2015


Dive into the research topics of 'A novel gene network analysis in liver tissues of diabetic rats in response to resistant starch treatment'. Together they form a unique fingerprint.

Cite this