Purpose: A better understanding of the underlying biology of invasive serous ovarian cancer is critical for the development of early detection strategies and new therapeutics. The objective of this study was to define gene expression patterns associated with favorable survival. Experimental Design: RNA from 65 serous ovarian cancers was analyzed using Affymetrix U133A microarrays.This included 54 stage III/IV cases (30 short-term survivors who lived <3 years and 24 long-term survivors who lived >7 years) and11stage I/II cases. Genes were screened on the basis of their level of and variability in expression, leaving 7,821for use in developing a predictive model for survival. A composite predictive model was developed that combines Bayesian classification tree and multivariate discriminant models. Leave-one-out cross-validation was used to select and evaluate models. Results: Patterns of genes were identified that distinguish short-term and long-term ovarian cancer survivors. The expression model developed for advanced stage disease classified all 11early-stage ovarian cancers as long-term survivors. The MAL gene, which has been shown to confer resistance to cancer therapy, was most highly overexpressed in short-term survivors (3-fold compared with long-term survivors, and 29-fold compared with early-stage cases).These results suggest that gene expression patterns underlie differences in outcome, and an examination of the genes that provide this discrimination reveals that many are implicated in processes that define the malignant phenotype. Conclusions: Differences in survival of advanced ovarian cancers are reflected by distinct patterns of gene expression.This biological distinction is further emphasized by the finding that earlystage cancers share expression patterns with the advanced stage long-term survivors, suggesting a shared favorable biology.
The antibiotic vancomycin is used as a last resort to treat persistent infections caused by Gram‐positive pathogens. Vancomycin kills bacteria by binding a peptidoglycan precursor, thereby inhibiting cell‐wall biosynthesis. An alarming type of resistance to this antibiotic comes in the form of vancomycin‐resistant Enterococci(VRE). VRE have acquired genes that allow them to remodel the cell‐wall precursor and prevent vancomycin binding. Expression of these remodeling genes is under control of the VanSR two‐component system. VanS is a membrane‐bound sensor kinase that recognizes the vancomycin signal, and in response activates the transcription factor VanR, which activates expression of the remodeling genes. However, very little is known about how VanS senses the antibiotic.
To date, nine different types of VRE have been discovered, with VanA and VanB types responsible for the vast majority of human infections. Since vancomycin can induce the expression of both VanA and VanB resistance genes, we hypothesize that the VanS proteins from these types are activated by directly binding to vancomycin. We used in vitro autokinase assays to show that vancomycin directly activates VanS from VanB VRE (VanSB), while having no direct effect on VanSA. We isolated the VanSB periplasmic sensor domain and used fluorescence anisotropy to show that it directly binds to a fluorescent vancomycin analog. Computational modeling predicts that the VanSB sensor domain adopts a PAS‐like fold, and HDX‐MS experiments supported this prediction and identified a potential vancomycin‐binding site. We also developed vancomycin photoprobes to confirm this binding site and to elucidate vancomycin’s orientation in the interaction. These results demonstrate how VanSBcan directly sense vancomycin in the environment to activate the resistance mechanism in VanB VRE, providing a promising therapeutic target to combat these dangerous pathogens.
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