Activation of NADPH oxidases leads to DNA damage in esophageal cells

Abstract Gastroesophageal reflux disease (GERD) is the strongest known risk factor for esophageal adenocarcinoma. In the center of tumorigenic events caused by GERD is repeated damage of esophageal tissues by the refluxate. In this study, we focused on a genotoxic aspect of exposure of esophageal cells to acidic bile reflux (BA/A). Analyzing cells generated from patients with Barrett’s esophagus and human esophageal specimens, we found that BA/A cause significant DNA damage that is mediated by reactive-oxygen species. ROS originate from mitochondria and NADPH oxidases. We specifically identified NOX1 and NOX2 enzymes to be responsible for ROS generation. Inhibition of NOX2 and NOX1 with siRNA or chemical inhibitors significantly suppresses ROS production and DNA damage induced by BA/A. Mechanistically, our data showed that exposure of esophageal cells to acidic bile salts induces phosphorylation of the p47phox subunit of NOX2 and its translocation to the cellular membrane. This process is mediated by protein kinase C, which is activated by BA/A. Taken together, our studies suggest that inhibition of ROS induced by reflux can be a useful strategy for preventing DNA damage and decreasing the risk of tumorigenic transformation caused by GERD

Systematically characterizing and prioritizing chemosensitivity related gene based on Gene Ontology and protein interaction network

<p>Abstract</p> <p>Background</p> <p>The identification of genes that predict in vitro cellular chemosensitivity of cancer cells is of great importance. Chemosensitivity related genes (CRGs) have been widely utilized to guide clinical and cancer chemotherapy decisions. In addition, CRGs potentially share functional characteristics and network features in protein interaction networks (PPIN).</p> <p>Methods</p> <p>In this study, we proposed a method to identify CRGs based on Gene Ontology (GO) and PPIN. Firstly, we documented 150 pairs of drug-CCRG (curated chemosensitivity related gene) from 492 published papers. Secondly, we characterized CCRGs from the perspective of GO and PPIN. Thirdly, we prioritized CRGs based on CCRGs’ GO and network characteristics. Lastly, we evaluated the performance of the proposed method.</p> <p>Results</p> <p>We found that CCRG enriched GO terms were most often related to chemosensitivity and exhibited higher similarity scores compared to randomly selected genes. Moreover, CCRGs played key roles in maintaining the connectivity and controlling the information flow of PPINs. We then prioritized CRGs using CCRG enriched GO terms and CCRG network characteristics in order to obtain a database of predicted drug-CRGs that included 53 CRGs, 32 of which have been reported to affect susceptibility to drugs. Our proposed method identifies a greater number of drug-CCRGs, and drug-CCRGs are much more significantly enriched in predicted drug-CRGs, compared to a method based on the correlation of gene expression and drug activity. The mean area under ROC curve (AUC) for our method is 65.2%, whereas that for the traditional method is 55.2%.</p> <p>Conclusions</p> <p>Our method not only identifies CRGs with expression patterns strongly correlated with drug activity, but also identifies CRGs in which expression is weakly correlated with drug activity. This study provides the framework for the identification of signatures that predict in vitro cellular chemosensitivity and offers a valuable database for pharmacogenomics research.</p