Project description:We used Nimblegen HD aCGH to detect copy-number variants between genomes of BN and SHR rats Comparison of single BN against single SHR individual in a dye-swap experiment
Project description:We used Nimblegen HD aCGH to detect copy-number variants between genomes of BN and M520 rats Comparison of single BN against single M520 individual in a dye-swap experiment
Project description:Male Sprague-Dawley rats were used to establish exhausted-exercise model by motorized rodent treadmill. Yu-Ping-Feng-San at doses of 2.18 g/kg was administrated by gavage before exercise training for 10 consecutive days. Quantitative proteomics was performed for assessing the related mechanism of Yu-Ping-Feng-San.
Project description:Living organisms are intricate systems with dynamic internal processes. Their RNA, protein, and metabolite levels fluctuate in response to variations in health and environmental conditions. Among these, RNA expression is particularly accessible for comprehensive analysis, thanks to the evolution of high throughput sequencing technologies in recent years. This progress has enabled researchers to identify unique RNA patterns associated with various diseases, as well as to develop predictive and prognostic biomarkers for therapy response. Such cross-sectional studies allow for the identification of differentially expressed genes (DEGs) between groups, but they have limitations. Specifically, they often fail to capture the temporal changes in gene expression following individual perturbations and may lead to significant false discoveries due to inherent noise in RNA sequencing sample preparation and data collection. To address these challenges, our study hypothesized that frequent, longitudinal RNA sequencing (RNAseq) analysis of blood samples could offer a more profound understanding of the temporal dynamics of gene expression in response to drug interventions, while also enhancing the accuracy of identifying genes influenced by these drugs. In this research, we conducted RNAseq on 829 blood samples collected from 84 Sprague-Dawley lab rats. Excluding the control group, each rat was administered one of four different compounds known for liver toxicity: tetracycline, isoniazid, valproate, and carbon tetrachloride. We developed specialized bioinformatics tools to pinpoint genes that exhibit temporal variation in response to these treatments.
