Theodor-Boveri-Institut für Biowissenschaften
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Obligate human pathogenic Neisseria gonorrhoeae are the second most frequent bacterial cause of sexually transmitted diseases. These bacteria invade different mucosal tissues and occasionally disseminate into the bloodstream. Invasion into epithelial cells requires the activation of host cell receptors by the formation of ceramide-rich platforms. Here, we investigated the role of sphingosine in the invasion and intracellular survival of gonococci. Sphingosine exhibited an anti-gonococcal activity in vitro. We used specific sphingosine analogs and click chemistry to visualize sphingosine in infected cells. Sphingosine localized to the membrane of intracellular gonococci. Inhibitor studies and the application of a sphingosine derivative indicated that increased sphingosine levels reduced the intracellular survival of gonococci. We demonstrate here, that sphingosine can target intracellular bacteria and may therefore exert a direct bactericidal effect inside cells.
Recently, several classifiers that combine primary tumor data, like gene expression data, and secondary data sources, such as protein-protein interaction networks, have been proposed for predicting outcome in breast cancer. In these approaches, new composite features are typically constructed by aggregating the expression levels of several genes. The secondary data sources are employed to guide this aggregation. Although many studies claim that these approaches improve classification performance over single genes classifiers, the gain in performance is difficult to assess. This stems mainly from the fact that different breast cancer data sets and validation procedures are employed to assess the performance. Here we address these issues by employing a large cohort of six breast cancer data sets as benchmark set and by performing an unbiased evaluation of the classification accuracies of the different approaches. Contrary to previous claims, we find that composite feature classifiers do not outperform simple single genes classifiers. We investigate the effect of (1) the number of selected features; (2) the specific gene set from which features are selected; (3) the size of the training set and (4) the heterogeneity of the data set on the performance of composite feature and single genes classifiers. Strikingly, we find that randomization of secondary data sources, which destroys all biological information in these sources, does not result in a deterioration in performance of composite feature classifiers. Finally, we show that when a proper correction for gene set size is performed, the stability of single genes sets is similar to the stability of composite feature sets. Based on these results there is currently no reason to prefer prognostic classifiers based on composite features over single genes classifiers for predicting outcome in breast cancer.