Anti-MED27 (full length) polyclonal antibody

This study aimed to explore the effect of MED27 on the expression of epithelial-mesenchymal transition (EMT)-related proteins and beta-catenin in adrenal cortical carcinoma (ACC). The functional mechanism of MED27 on ACC processes was also explored. The expression of MED27 was assessed by quantitative real-time polymerase chain reaction (qRT-PCR). siRNA was utilized to knockdown the expression of MED27. CCK8 assays were performed to evaluate SW-13 cell proliferation. Transwell assays were performed to assess the invasion ability, and wound healing assays were utilized to detect migration. A tumor xenograft mouse model was established to investigate the impact of silencing MED27 on tumor growth and metastasis. MED27 was highly expressed in ACC tissues and cells. Down-regulation of MED27 induced ACC cell apoptosis, and significantly attenuated ACC cell proliferation, invasion and metastasis in vivo and in vitro. MED27 knockdown regulated the expression of EMT-related proteins and Wnt/beta-catenin signaling pathway-related proteins. Our study investigated the function and mechanism of MED27 and validated that MED27 plays a negative role in ACC occurrence and progression and could be utilized as a new therapeutic target in ACC prevention and treatment.

Background: Chemical or small interfering (si) RNA screens measure the effects of many independent experimental conditions, each applied to a population of cells (e.g., all of the cells in a well). High-content screens permit a readout (e.g., fluorescence, luminescence, cell morphology) from each cell in the population. Most analysis approaches compare the average effect on each population, precluding identification of outliers that affect the distribution of the reporter in the population but not its average. Other approaches only measure changes to the distribution with a single parameter, precluding accurate distinction and clustering of interesting outlier distributions. Results: We describe a methodology to identify outlier conditions by considering the cell-level measurements from each condition as a sample of an underlying distribution. With appropriate selection of a distance metric, all effects can be embedded in a fixed-dimensionality Euclidean basis, facilitating identification and clustering of biologically interesting outliers. We demonstrate that measurement of distances with the Hellinger distance metric offers substantial computational efficiencies over alternative metrics. We validate this methodology using an RNA interference (RNAi) screen in mouse embryonic stem cells (ESC) with a Nanog reporter. The methodology clusters effects of multiple control siRNAs into their true identities better than conventional approaches describing the median cell fluorescence or the commonly used Kolmogorov-Smirnov distance between the observed fluorescence distribution and the null distribution. It identifies outlier genes with effects on the reporter distribution that would have been missed by other methods. Among them, siRNA targeting Chek1 leads to a wider Nanog reporter fluorescence distribution. Similarly, siRNA targeting Med14 or Med27 leads to a narrower Nanog reporter fluorescence distribution. We confirm the roles of these three genes in regulating pluripotency by mRNA expression and alkaline phosphatase staining using independent short hairpin (sh) RNAs. Conclusions: Using our methodology, we describe each experimental condition by a probability distribution. Measuring distances between probability distributions permits a multivariate rather than univariate readout. Clustering points derived from these distances allows us to obtain greater biological insight than methods based solely on single parameters. We find several outliers from a mouse ESC RNAi screen that we confirm to be pluripotency regulators. Many of these outliers would have been missed by other analysis methods.