P73 regulates cisplatin-induced apoptosis in ovarian cancer cells via a calcium/calpain-dependent mechanism

P73 is important in drug-induced apoptosis in some cancer cells, yet its role in the regulation of chemosensitivity in ovarian cancer (OVCA) is poorly understood. Furthermore, if and how the deregulation of p73-mediated apoptosis confers resistance to cisplatin (CDDP) treatment is unclear. Here we demonstrate that TAp73α over-expression enhanced CDDP-induced PARP cleavage and apoptosis in both chemosensitive (OV2008 and A2780s) and their resistant counterparts (C13* and A2780cp) and another chemoresistant OVCA cells (Hey); in contrast, the effect of ΔNp73α over-expression was variable. P73α downregulation attenuated CDDP-induced PUMA and NOXA upregulation and apoptosis in OV2008 cells. CDDP decreased p73α steady-state protein levels in OV2008, but not in C13*, although the mRNA expression was identical. CDDP-induced p73α downregulation was mediated by a calpain-dependent pathway. CDDP induced calpain activation and enhanced its cytoplasmic interaction and co-localization with p73α in OV2008, but not C13* cells. CDDP increased the intracellular calcium concentration ([Ca2+]i) in OV2008 but not C13* whereas cyclopiazonic acid (CPA), a Ca2+-ATPase inhibitor, caused this response and calpain activation, p73α processing and apoptosis in both cell types. CDDP-induced [Ca2+]i increase in OV2008 cells was not effected by the elimination of extracellular Ca2+, but this was attenuated by the depletion of internal Ca2+ store, indicating that mobilization of intracellular Ca2+] stores was potentially involved. These findings demonstrate that p73α and its regulation by the Ca2+-mediated calpain pathway are involved in CDDP-induced apoptosis in OVCA cells and that dysregulation of Ca2+/calpain/p73 signaling may in part be the pathophysiology of CDDP resistance. Understanding the cellular and molecular mechanisms of chemoresistance will direct the development of effective strategies for the treatment of chemoresistant OVCA

Modelling non Measurable Processes by Neural Networks: Forecasting Underground Flow Case Study of the Céze Basin (Gard - France)

International audienceAfter a presentation of the nonlinear properties of neural networks, their applications to hydrology are described. A neural predictor is satisfactorily used to estimate a flood peak. The main contribution of the paper concerns an original method for visualising a hidden underground flow Satisfactory experimental results were obtained that fitted well with the knowledge of local hydrogeology, opening up an interesting avenue for modelling using neural networks

Relationship between the geometrical and structural properties of layered fractured rocks and their effective permeability tensor. A simulation study

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Intégration de l’estimation de l’humidité du bassin dans la prévision des crues éclair par apprentissage statistique

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Ensemble model to enhance robustness of flash flood forecasting using an Artificial Neural Network: case-study on the Gardon Basin (south-eastern France)

International audienceDuring the last few decades neural networks have been increasingly used in hydrological modelling for theirfundamental property of parsimony and of universal approximation of non-linear functions. For the purposeof flash flood forecasting, feed-forward and recurrent multi-layer perceptrons appear to be efficient tools.Nevertheless, their forecasting performances are sensitive to the initialization of the network parameters. Wehave studied the cross-validation efficiency to select initialization providing the best forecasts in real time situation.Sensitivity to initialization of feed-forward and recurrent models is compared for one-hour lead-timeforecasts. This study shows that cross-validation is unable to select the best initialization. A more robustmodel has been designed using the median of several models outputs; in this context, this paper analysesthe design of the ensemble model for both recurrent and feed-forward models

Modelo de “ensemble” para incrementar la robusted de la predicción de avenidas utilizando redes neuronales artificiales: aplicación a la cuenca Gardon (sureste de Francia)

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