High-intensity interval training (HIIT) effectively enhances heart function via miR-195 dependent cardiomyopathy reduction in high-fat high-fructose diet-induced diabetic rats.

Aims: Regarding the fact that up-regulation of miR-195 in diabetic hearts has a potential role in diabetic cardiomyopathy, the present study investigated whether continuous endurance training (CET) and high-intensity interval training (HIIT) reduces miR-195 expression and which exercise is effective in this regard.Methods: Diabetes was induced by high-fat high-fructose diet (HFHFD). Then, the rats were sub-divided into three categories; sedentary (HFHFD + SED), continuous endurance training (HFHFD + CET), and high-intensity interval training group (HFHFD + HIIT). After eight weeks of running, expression of miR-195 and myocardial function were evaluated.Results: HIIT effectively decreases the expression of miR-195 and increases the expression of Sirt1 and BCL-2 in diabetic rats compared with CET. Our results showed that HIIT compared with CET increases left ventricular ejection fraction (LVEF%) and fractional shortening (FS%).Conclusions: Our results indicated that exercise, especially HIIT is an appropriate strategy for reducing miR-195 and improving myocardial function in diabetic rats compared with CET.

Assessment of gene set analysis methods based on microarray data.

Gene set analysis (GSA) incorporates biological information into statistical knowledge to identify gene sets differently expressed between two or more phenotypes. It allows us to gain an insight into the functional working mechanism of cells beyond the detection of differently expressed gene sets. In order to evaluate the competence of GSA approaches, three self-contained GSA approaches with different statistical methods were chosen; Category, Globaltest and Hotelling's T(2) together with their assayed power to identify the differences expressed via simulation and real microarray data. The Category does not take care of the correlation structure, while the other two deal with correlations. In order to perform these methods, R and Bioconductor were used. Furthermore, venous thromboembolism and acute lymphoblastic leukemia microarray data were applied. The results of three GSAs showed that the competence of these methods depends on the distribution of gene expression in a dataset. It is very important to assay the distribution of gene expression data before choosing the GSA method to identify gene sets differently expressed between phenotypes. On the other hand, assessment of common genes among significant gene sets indicated that there was a significant agreement between the result of GSA and the findings of biologists.