Effect of Soyabean Isoflavones Exposure on Onset of Puberty, Serum Hormone Concentration and Gene Expression in Hypothalamus, Pituitary Gland and Ovary of Female Bama Miniature Pigs

This study was to investigate the effect of soyabean isoflavones (SIF) on onset of puberty, serum hormone concentration, and gene expression in hypothalamus, pituitary and ovary of female Bama miniature pigs. Fifty five, 35-days old pigs were randomly assigned into 5 treatment groups consisting of 11 pigs per treatment. Results showed that dietary supplementation of varying dosage (0, 250, 500, and 1,250 mg/kg) of SIF induced puberty delay of the pigs with the age of puberty of pigs fed basal diet supplemented with 1,250 mg/kg SIF was significantly higher (p<0.05) compared to control. Supplementation of SIF or estradiol valerate (EV) reduced (p<0.05) serum gonadotrophin releasing hormone and luteinizing hormone concentration, but increased follicle-stimulating hormone concentration in pigs at 4 months of age. The expression of KiSS-1 metastasis-suppressor (KISS1), steroidogenic acute regulatory protein (StAR) and 3-beta-hydroxysteroid dehydrogenase/delta-5-delta-4 isomerase (3β-HSD) was reduced (p<0.01) in SIF-supplemented groups. Expression of gonadotropin-releasing hormone receptor in the pituitary of miniature pigs was reduced (p<0.05) compared to the control when exposed to 250, 1,250 mg/kg SIF and EV. Pigs on 250 mg/kg SIF and EV also showed reduced (p<0.05) expression of cytochrome P450 19A1 compared to the control. Our results indicated that dietary supplementation of SIF induced puberty delay, which may be due to down-regulation of key genes that play vital roles in the synthesis of steroid hormones

Prediction of Drought-Resistant Genes in Arabidopsis thaliana Using SVM-RFE

Background: Identifying genes with essential roles in resisting environmental stress rates high in agronomic importance. Although massive DNA microarray gene expression data have been generated for plants, current computational approaches underutilize these data for studying genotype-trait relationships. Some advanced gene identification methods have been explored for human diseases, but typically these methods have not been converted into publicly available software tools and cannot be applied to plants for identifying genes with agronomic traits. Methodology: In this study, we used 22 sets of Arabidopsis thaliana gene expression data from GEO to predict the key genes involved in water tolerance. We applied an SVM-RFE (Support Vector Machine-Recursive Feature Elimination) feature selection method for the prediction. To address small sample sizes, we developed a modified approach for SVM-RFE by using bootstrapping and leave-one-out cross-validation. We also expanded our study to predict genes involved in water susceptibility. Conclusions: We analyzed the top 10 genes predicted to be involved in water tolerance. Seven of them are connected to known biological processes in drought resistance. We also analyzed the top 100 genes in terms of their biological functions. Our study shows that the SVM-RFE method is a highly promising method in analyzing plant microarray data for studyin