Fatty Acid Synthase Inhibitors Enhance Microtubule-Stabilizing and Microtubule-Destabilizing Drugs in Taxane-Resistant Prostate Cancer Cells.

Prostate cancer is the third leading cause of cancer-related death in men in the United States. Taxane chemotherapy is a staple therapy for men with metastatic prostate cancer, yet the median survival is less than 2 years in this setting. New strategies are needed to overcome taxane resistance to improve patient survival. Fatty acid synthase (FASN) is overexpressed in many types of cancer, and several inhibitors have been designed in the past 30 years. Previously, we showed that the FASN inhibitor orlistat was able to synergize with taxanes in two established taxane-resistant (TxR) cell lines. In the current study, we investigated five FASN inhibitors-cerulenin, orlistat, triclosan, thiophenopyrimidine fasnall, and pyrazole derivative TVB-3166 for their potential to synergize with docetaxel (a microtubule stabilizer) and vinblastine (a microtubule destabilizer) in TxR cell lines. Orlistat, TVB-3166, and fasnall synergistically inhibited cell viability when combined with docetaxel and vinblastine in PC3-TxR and DU145-TxR cells. Confocal microscopy and immunoblot with an antidetyrosinated tubulin antibody demonstrated that enhanced microtubule stability was induced by the combined treatment of FASN inhibitors and docetaxel compared with docetaxel alone, while combinations of FASN inhibitors with vinblastine diminished microtubule stability compared to vinblastine alone.

Fluorescent Sensor Arrays Can Predict and Quantify the Composition of Multicomponent Bacterial Samples.

Fast and reliable identification of infectious disease agents is among the most important challenges for the healthcare system. The discrimination of individual components of mixed infections represents a particularly difficult task. In the current study we further expand the functionality of a ratiometric sensor array technology based on small-molecule environmentally-sensitive organic dyes, which can be successfully applied for the analysis of mixed bacterial samples. Using pattern recognition methods and data from pure bacterial species, we demonstrate that this approach can be used to quantify the composition of mixtures, as well as to predict their components with the accuracy of ~80% without the need to acquire additional reference data. The described approach significantly expands the functionality of sensor arrays and provides important insights into data processing for the analysis of other complex samples.