Association of single nucleotide polymorphisms in the microRNA miR‐1596 locus with residual feed intake in chickens

MicroRNAs are an abundant class of small non‐coding RNAs that regulate gene expression. Genetic variations in microRNA sequences may be associated with phenotype differences by influencing the expression of microRNAs and/or their targets. This study identified two single nucleotide polymorphisms (SNPs) in the genomic region of the microRNA miR‐1596 locus of chicken. Of the two SNPs, one was 95 bp upstream of miR‐1596 (g.5678784A>T) and the other was in the middle of the sequence producing the mature microRNA gga‐miR‐1596‐3p (g.5678944A>G). Genotypic distribution of the two SNPs had large differences among 12 chicken breeds (lines), especially between the fast‐growing commercial lines and the slow‐growing Chinese indigenous breeds for the g.5678784A>T SNP. Only the g.5678784A>T SNP was significantly associated with residual feed intake (RFI) in the F₂ population derived from a fast‐growing and a slow‐growing broiler as well as in the pure Huiyang bearded chicken. The birds with the AA genotype of the g.5678784A>T SNP had lower RFI and higher expression of the mature gga‐miR‐1596‐3p microRNA of miR‐1596 than did those with the other genotypes of the same SNP. We also found that the expression of the mature gga‐miR‐1596‐3p microRNA of miR‐1596 was significantly associated with RFI. These findings suggest that miR‐1596 can become a candidate gene related to RFI, and its genetic variation may contribute to changes in RFI by altering expression levels of the mature gga‐miR‐1596‐3p microRNA in chicken.     

Contribution of genetic and epigenetic mechanisms to Wnt pathway activity in prevalent skeletal disorders

We reported previously that the expression of Wnt-related genes is lower in osteoporotic hip fractures than in osteoarthritis. We aimed to confirm those results by analyzing β-catenin levels and explored potential genetic and epigenetic mechanisms involved.     

An ANN-GA model based promoter prediction in Arabidopsis thaliana using tilling microarray data

Identification of promoter region is an important part of gene annotation. Identification of promoters in eukaryotes is important as promoters modulate various metabolic functions and cellular stress responses. In this work, a novel approach utilizing intensity values of tilling microarray data for a model eukaryotic plant Arabidopsis thaliana, was used to specify promoter region from non-promoter region. A feed-forward back propagation neural network model supported by genetic algorithm was employed to predict the class of data with a window size of 41. A dataset comprising of 2992 data vectors representing both promoter and non-promoter regions, chosen randomly from probe intensity vectors for whole genome of Arabidopsis thaliana generated through tilling microarray technique was used. The classifier model shows prediction accuracy of 69.73% and 65.36% on training and validation sets, respectively. Further, a concept of distance based class membership was used to validate reliability of classifier, which showed promising results. The study shows the usability of micro-array probe intensities to predict the promoter regions in eukaryotic genomes.     

病毒基因组启动子识别的人工神经网络方法

本文运用神经网络方法,并结合病毒基因分子生物学有关理论与统计事实,对病毒基因启动子区域进行了识别,文中选择了共35个基因组,作为研究对象．学习组选择了28个基因组,预测组选择了7个基因组,结果表明,将神经网络模型与病毒基因有关理论相结合,能够运用计算方法,以大量的可能启动子组合中排列出唯一的启动子区域．