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Cisplatin, a commonly used chemotherapeutic, is associated with ototoxicity, renal toxicity and neurotoxicity, thus identifying means to increase the therapeutic index of cisplatin may allow for improved outcomes. A SNP (rs4343077) within EPS8, discovered through a genome wide association study of cisplatin-induced cytotoxicity and apoptosis in lymphoblastoid cell lines (LCLs), provided impetus to further study this gene. The purpose of this work was to evaluate the role of EPS8 in cellular susceptibility to cisplatin in cancerous and non-cancerous cells. We used EPS8 RNA interference to determine the effect of decreased EPS8 expression on LCL and A549 lung cancer cell sensitivity to cisplatin. EPS8 knockdown in LCLs resulted in a 7.9% increase in cisplatin-induced survival (P = 1.98×10−7) and an 8.7% decrease in apoptosis (P = 0.004) compared to control. In contrast, reduced EPS8 expression in lung cancer cells resulted in a 20.6% decrease in cisplatin-induced survival (P = 5.08×10−5). We then investigated an EPS8 inhibitor, mithramycin A, as a potential agent to increase the therapeutic index of cisplatin. Mithramycin A decreased EPS8 expression in LCLs resulting in decreased cellular sensitivity to cisplatin as evidenced by lower caspase 3/7 activation following cisplatin treatment (42.7%±6.8% relative to control P = 0.0002). In 5 non-small-cell lung carcinoma (NSCLC) cell lines, mithramycin A also resulted in decreased EPS8 expression. Adding mithramycin to 4 NSCLC cell lines and a bladder cancer cell line, resulted in increased sensitivity to cisplatin that was significantly more pronounced in tumor cell lines than in LCL lines (p<0.0001). An EGFR mutant NSCLC cell line (H1975) showed no significant change in sensitivity to cisplatin with the addition of mithramycin treatment. Therefore, an inhibitor of EPS8, such as mithramycin A, could improve cisplatin treatment by increasing sensitivity of tumor relative to normal cells. [ABSTRACT FROM AUTHOR]

Respiratory infections stemming from influenza viruses and the Severe Acute Respiratory Syndrome corona virus (SARS-CoV) represent a serious public health threat as emerging pandemics. Despite efforts to identify the critical interactions of these viruses with host machinery, the key regulatory events that lead to disease pathology remain poorly targeted with therapeutics. Here we implement an integrated network interrogation approach, in which proteome and transcriptome datasets from infection of both viruses in human lung epithelial cells are utilized to predict regulatory genes involved in the host response. We take advantage of a novel “crowd-based” approach to identify and combine ranking metrics that isolate genes/proteins likely related to the pathogenicity of SARS-CoV and influenza virus. Subsequently, a multivariate regression model is used to compare predicted lung epithelial regulatory influences with data derived from other respiratory virus infection models. We predicted a small set of regulatory factors with conserved behavior for consideration as important components of viral pathogenesis that might also serve as therapeutic targets for intervention. Our results demonstrate the utility of integrating diverse ‘omic datasets to predict and prioritize regulatory features conserved across multiple pathogen infection models. [ABSTRACT FROM AUTHOR]

Demography impacts the observed standing level of genetic diversity present in populations. Distinguishing the relative impacts of demography from selection requires a baseline of expressed gene variation in naturally occurring populations. Six nuclear genes were sequenced to estimate the patterns and levels of genetic diversity in natural Arabidopsis lyrata subsp. petraea populations that differ in demographic histories since the Pleistocene. As expected, northern European populations have genetic signatures of a strong population bottleneck likely due to glaciation during the Pleistocene. Levels of diversity in the northern populations are about half of that in central European populations. Bayesian estimates of historical population size changes indicate that central European populations also have signatures of population size change since the last glacial maxima, suggesting that these populations are not as stable as previously thought. Time since divergence amongst northern European populations is higher than amongst central European populations, suggesting that the northern European populations were established before the Pleistocene and survived glaciation in small separated refugia. Estimates of demography based on expressed genes are complementary to estimates based on microsatellites and transposable elements, elucidating temporal shifts in population dynamics and confirming the importance of marker selection for tests of demography. [ABSTRACT FROM AUTHOR]