Effects of interacting networks of cardiovascular risk genes on the risk of type 2 diabetes mellitus (the CODAM study)

Epigallocatechin-3-gallate ameliorates glucolipid metabolism and oxidative stress in type 2 diabetic rats

AIMS

The objective of this study was to explore the effects of epigallocatechin-3-gallate (EGCG) on type 2 diabetes mellitus (T2DM).

MAIN METHODS

Male Sprague-Dawley rats were allocated into six groups. The control group received a conventional diet. The diabetic group received a high-sucrose high-fat (HSHF) diet for 4 weeks and then was fasted and injected with streptozotocin (STZ); subsequently, the rats received a HSHF diet for another 4 weeks to develop diabetes. The four treatment groups were diabetic rats that received intragastric metformin (500 mg/kg/day) or EGCG (25, 50, and 100 mg/kg/day) for 10 weeks. All groups except the control group received a HSHF diet throughout the experiment. Several biochemical parameters such as fasting blood glucose (FBG), postprandial blood glucose (PBG), liver glycogen, muscle glycogen, fasting serum insulin (FSI), homeostasis model of insulin resistance (HOMA-IR), total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), free fatty acids (FFA), superoxide dismutase (SOD), and malondialdehyde (MDA) were measured to assess the effects of EGCG on glycemic control, insulin resistance, lipid profile, and oxidative stress. Furthermore, oxidative stress in pancreatic islet β cells was detected by dihydroethidium staining.

KEY FINDINGS

A HSHF diet and STZ injection induced T2DM, as indicated by changed blood glucose and body weight, which was accompanied by insulin resistance, an altered lipid profile, and oxidative stress. Interestingly, EGCG treatment dose-dependently recovered these indexes.

SIGNIFICANCE

EGCG successfully ameliorated glycemic control and insulin sensitivity while reducing the lipid profile and oxidative stress in a T2DM rat model.

Inferring gene regulatory networks from asynchronous microarray data with AIRnet

Background

Modern approaches to treating genetic disorders, cancers and even epidemics rely on a detailed understanding of the underlying gene signaling network. Previous work has used time series microarray data to infer gene signaling networks given a large number of accurate time series samples. Microarray data available for many biological experiments is limited to a small number of arrays with little or no time series guarantees. When several samples are averaged to examine differences in mean value between a diseased and normal state, information from individual samples that could indicate a gene relationship can be lost.

Results

Asynchronous Inference of Regulatory Networks (AIRnet) provides gene signaling network inference using more practical assumptions about the microarray data. By learning correlation patterns for the changes in microarray values from all pairs of samples, accurate network reconstructions can be performed with data that is normally available in microarray experiments.

Conclusions

By focussing on the changes between microarray samples, instead of absolute values, increased information can be gleaned from expression data.