Positive and Negative Regulation of Prostate Stem Cell Antigen Expression by Yin Yang 1 in Prostate Epithelial Cell Lines

Prostate cancer is influenced by epigenetic modification of genes involved in cancer development and progression. Increased expression of Prostate Stem Cell Antigen (PSCA) is correlated with development of malignant human prostate cancer, while studies in mouse models suggest that decreased PSCA levels promote prostate cancer metastasis. These studies suggest that PSCA has context-dependent functions, and could be differentially regulated during tumor progression. In the present study, we identified the multi-functional transcription factor Yin Yang 1 (YY1) as a modulator of PSCA expression in prostate epithelial cell lines. Increased YY1 levels are observed in prostatic intraepithelial neoplasia (PIN) and advanced disease. We show that androgen-mediated up-regulation of PSCA in prostate epithelial cell lines is dependent on YY1. We identified two direct YY1 binding sites within the PSCA promoter, and showed that the upstream site inhibited, while the downstream site, proximal to the androgen-responsive element, stimulated PSCA promoter activity. Thus, changes in PSCA expression levels in prostate cancer may at least partly be affected by cellular levels of YY1. Our results also suggest multiple roles for YY1 in prostate cancer which may contribute to disease progression by modulation of genes such as PSCA

Structure-Based Virtual Screening for Drug Discovery: a Problem-Centric Review

Structure-based virtual screening (SBVS) has been widely applied in early-stage drug discovery. From a problem-centric perspective, we reviewed the recent advances and applications in SBVS with a special focus on docking-based virtual screening. We emphasized the researchers’ practical efforts in real projects by understanding the ligand-target binding interactions as a premise. We also highlighted the recent progress in developing target-biased scoring functions by optimizing current generic scoring functions toward certain target classes, as well as in developing novel ones by means of machine learning techniques