Project description:The objective of this study was to identify changes in gene expression levels between wild-type and AE2-knockout colon. 7 wild-type and 7 AE2-null colon RNA samples were compared to identify genes that are differentially expressed in the colon of AE2 knockout mice. Total RNA was collected from both male and female wild-type and AE2-null colons (including both proximal and distal colon) when the mice were 18 days old. All pairs were gender- and aged-matched, and all mice were on a mixed 129 Black Swiss background. To facilitate statistical analysis and reduce any affects of Cy3 and Cy5 labeling, comparisons of two wild-type and two AE2-null samples were repeated using a dye flip.
Project description:To further understand different gene expression of islr knockout mouse colon and normal colon, we have employed colon samples microarray expression profiling as a discovery platform to identify different genes with Islr knockout mouse colon and normal colon. Comparison with normal colon, significantly upgene is 779 and downgene is 996 in knockout group.
Project description:To further understand different gene expression of miR-31 knockout mouse colon and normal colon, we have employed colonic epithelium microarray expression profiling as a discovery platform to identify different genes with miR-31 knockout mouse colon and normal colon.comparision with normal colonic epithelium,upgene is 285 and downgene is 178 in knockout group.
Project description:In the boundaries of chromosome-centric Human Proteome Project c-HPP to obtain information about proteoforms coded by chromosome 18, several cell lines were analyzed. In our study we have been using proteoform separation by 2DE (sectional analysis) and semi-virtual 2DE with following shotgun mass-spectrometry using LC ESI-MS/MS. Previously, we published a first draft of this research, where only HepG2 cells were used. Here, next step was done using liver, glioblastoma, fibroblasts, kidney cells and plasma. Altogether, confident (2 significant sequences minimum) information about proteoforms coded by ~80 genes was obtained. The 3D-graphs showing distribution of different proteoforms from the same gene in 2D map were generated. Additionally, semi-virtual 2DE approach allowed for detecting more proteoforms and estimate their pI more precisely