Project description:EP300, a transcriptional co-activator of E-cadherin, has been recently found by our group to regulate doxorubicin resistance via by-pass of senescence and paclitaxel resistance by overcoming apoptosis in a minimally transformed mammary epithelial cells (MTMEC). Moreover, EP300 deleted MTMEC cells exhibit an multi-drug resistant (MDR) phenotype independent of P-glycoprotein (ABCB1), an efflux pump or ABC drug transporter. This whole transcriptome array study was undertaken in order to explore the downstream targets in the EP300-mediated drug resistance, epidermal-to-mesenchymal transition and cancer stem cell phenotypes in breast cancer cell line MCF7.
Project description:We knocked down EP300 and examined the expression of lncRNA625 target genes. Gene expression profiling of knockdown samples on cDNA microarrays indicated that EP300 affected expression of several lncRNA625 downstream target genes
Project description:The goal of this study was to identify genes that were differentially regulated by ATF2 in TAMR cells (tamoxifen-resistant MCF7 derivatives) when compared to the tamoxifen-sensitive MCF7.
Project description:We knocked down EP300 and examined the expression of lncRNA625 target genes. Gene expression profiling of knockdown samples on cDNA microarrays indicated that EP300 affected expression of several lncRNA625 downstream target genes Stably-transfected KYSE150, transfected with shlncRNA625 or shscramble, were collected and lysed in TRIzol (Life technologies). Microarray experiments were performed following the Affymetrix protocol at the Shanghai Biotechnology Corporation.
Project description:Anticancer chemotherapy is an essential part of cancer treatment, but the emergence of resistance remains a major hurdle. Metabolic reprogramming is a notable phenotype associated with the acquisition of drug resistance. Here, we develop a computational framework that predicts metabolic gene targets capable of reverting the metabolic state of drug-resistant cells to that of drug-sensitive parental cells, thereby sensitizing the resistant cells. The computational framework performs single-gene knockout simulation of genome-scale metabolic models that predicts genome-wide metabolic flux distribution in drug-resistant cells, and clusters the resulting knockout flux data using uniform manifold approximation and projection. From the clustering analysis, knockout genes that lead to the flux data near that of drug-sensitive cells are considered drug sensitization targets. This computational approach is demonstrated using doxorubicin- and paclitaxel-resistant MCF7 breast cancer cells. Drug sensitization targets are further refined based on proteome and metabolome data, which generate GOT1 for doxorubicin-resistant MCF7, GPI for paclitaxel-resistant MCF7, and SLC1A5 as a common target. These targets are experimentally validated where inhibiting their expression results in increased sensitivity of drug-resistant cells to doxorubicin or paclitaxel. Taken together, the computational framework predicts drug sensitization targets in an intuitive and cost-efficient manner and can be applied to overcome drug-resistant cells associated with various cancers and other metabolic diseases.
Project description:Two metaplastic breast cancer cell lines were used: a widely available, HS578T, and a novel line isolated from a metaplastic breast cancer tumor, BAS. Doxorubicin and paclitaxel resistant derivatives of these lines were generated and transcriptome profiling performed.
Project description:Breast cancer cells and two metaplastic breast cancer cell lines were used: a widely available, HS578T, and a novel line isolated from a metaplastic breast cancer tumor, BAS. Doxorubicin and paclitaxel resistant derivatives of these metaplastic lines were generated and miR profiling performed.
