Quorum sensing network in clinical strains of A. baumannii : AidA is a new quorum quenching enzyme

Acinetobacter baumannii is an important pathogen that causes nosocomial infections generally associated with high mortality and morbidity in Intensive Care Units (ICUs). Currently, little is known about the Quorum Sensing (QS)/Quorum Quenching (QQ) systems of this pathogen. We analyzed these mechanisms in seven clinical isolates of A. baumannii. Microarray analysis of one of these clinical isolates, Ab1 (A. baumannii ST-2-clon-2010), previously cultured in the presence of 3-oxo-C12-HSL (a QS signalling molecule) revealed a putative QQ enzyme (α/β hydrolase gene, AidA). This QQ enzyme was present in all nonmotile clinical isolates (67% of which were isolated from the respiratory tract) cultured in nutrient depleted LB medium. Interestingly, this gene was not located in the genome of the only motile clinical strain growing in this medium (A. baumannii strain Ab421-GEIH-2010 [Ab7], isolated from a blood sample). The AidA protein expressed in E. coli showed QQ activity. Finally, we observed downregulation of the AidA protein (QQ system attenuation) in the presence of HO (ROS stress). In conclusion, most of the A. baumannii clinical strains were not surface motile (84%) and were of respiratory origin (67%). Only the pilT gene was involved in surface motility and related to the QS system. Finally, a new QQ enzyme (α/β hydrolase gene, AidA protein) was detected in these strains

Cancer network activity associated with therapeutic response and synergism

Background: Cancer patients often show no or only modest benefit from a given therapy. This major problem in oncology is generally attributed to the lack of specific predictive biomarkers, yet a global measure of cancer cell activity may support a comprehensive mechanistic understanding of therapy efficacy. We reasoned that network analysis of omic data could help to achieve this goal. Methods: A measure of “cancer network activity” (CNA) was implemented based on a previously defined network feature of communicability. The network nodes and edges corresponded to human proteins and experimentally identified interactions, respectively. The edges were weighted proportionally to the expression of the genes encoding for the corresponding proteins and relative to the number of direct interactors. The gene expression data corresponded to the basal conditions of 595 human cancer cell lines. Therapeutic responses corresponded to the impairment of cell viability measured by the half maximal inhibitory concentration (IC50) of 130 drugs approved or under clinical development. Gene ontology, signaling pathway, and transcription factor-binding annotations were taken from public repositories. Predicted synergies were assessed by determining the viability of four breast cancer cell lines and by applying two different analytical methods. Results: The effects of drug classes were associated with CNAs formed by different cell lines. CNAs also differentiate target families and effector pathways. Proteins that occupy a central position in the network largely contribute to CNA. Known key cancer-associated biological processes, signaling pathways, and master regulators also contribute to CNA. Moreover, the major cancer drivers frequently mediate CNA and therapeutic differences. Cell-based assays centered on these differences and using uncorrelated drug effects reveals novel synergistic combinations for the treatment of breast cancer dependent on PI3K-mTOR signaling. Conclusions: Cancer therapeutic responses can be predicted on the basis of a systems-level analysis of molecular interactions and gene expression. Fundamental cancer processes, pathways, and drivers contribute to this feature, which can also be exploited to predict precise synergistic drug combinations