Activation of p53 by nutlin-3a induces apoptosis and cellular senescence in human glioblastoma multiforme

Glioblastoma multiforme (GBM) is the most common and aggressive primary brain tumor in adults. Despite concerted efforts to improve current therapies and develop novel clinical approaches, patient survival remains poor. As such, increasing attention has focused on developing new therapeutic strategies that specifically target the apoptotic pathway in order to improve treatment responses. Recently, nutlins, small-molecule antagonists of MDM2, have been developed to inhibit p53-MDM2 interaction and activate p53 signaling in cancer cells. Glioma cell lines and primary cultured glioblastoma cells were treated with nutlin-3a. Nutlin-3a induced p53-dependent G1- and G2-M cell cycle arrest and apoptosis in glioma cell lines with normal TP53 status. In addition, nutlin-arrested glioma cells show morphological features of senescence and persistent induction of p21 protein. Furthermore, senescence induced by nutlin-3a might be depending on mTOR pathway activity. In wild-type TP53 primary cultured cells, exposure to nutlin-3a resulted in variable degrees of apoptosis as well as cellular features of senescence. Nutlin-3a-induced apoptosis and senescence were firmly dependent on the presence of functional p53, as revealed by the fact that glioblastoma cells with knockdown p53 with specific siRNA, or cells with mutated or functionally impaired p53 pathway, were completely insensitive to the drug. Finally, we also found that nutlin-3a increased response of glioma cells to radiation therapy. The results provide a basis for the rational use of MDM2 antagonists as a novel treatment option for glioblastoma patients.

TP53 induced glycolysis and apoptosis regulator (TIGAR) knockdown results in radiosensitization of glioma cells

Background and purpose: The TP53 induced glycolysis and apoptosis regulator (TIGAR) functions to lower fructose-2,6-bisphosphate (Fru-2,6-P2) levels in cells, consequently decreasing glycolysis and leading to the scavenging of reactive oxygen species (ROS), which correlate with a higher resistance to cell death. The decrease in intracellular ROS levels in response to TIGAR may also play a role in the ability of p53 to protect from the accumulation of genomic lesions. Given these good prospects of TIGAR for metabolic regulation and p53-response modulation, we analyzed the effects of TIGAR knockdown in U87MG and T98G glioblastoma-derived cell lines. Methods/results: After TIGAR-knockdown in glioblastoma cell lines, different metabolic parameters were assayed, showing an increase in Fru-2,6-P2, lactate and ROS levels, with a concomitant decrease in reduced glutathione (GSH) levels. In addition, cell growth was inhibited without evidence of apoptotic or autophagic cell death. In contrast, a clear senescent phenotype was observed. We also found that TIGAR protein levels were increased shortly after irradiation. In addition, avoiding radiotherapy-triggered TIGAR induction by gene silencing resulted in the loss of capacity of glioblastoma cells to form colonies in culture and the delay of DNA repair mechanisms, based in c-H2AX foci, leading cells to undergo morphological changes compatible with a senescent phenotype. Thus, the results obtained raised the possibility to consider TIGAR as a therapeutic target to increase radiotherapy effects. Conclusion: TIGAR abrogation provides a novel adjunctive therapeutic strategy against glial tumors by increasing radiation-induced cell impairment, thus allowing the use of lower radiotherapeutic doses.

Cómo aplicar árboles de decisión en SPSS

Un árbol de decisión es una forma gráfica y analítica de representar todos los eventos (sucesos) que pueden surgir a partir de una decisión asumida en cierto momento. Nos ayudan a tomar la decisión más"acertada", desde un punto de vista probabilístico, ante un abanico de posibles decisiones. Estos árboles permiten examinar los resultados y determinar visualmente cómo fluye el modelo. Los resultados visuales ayudan a buscar subgrupos específicos y relaciones que tal vez no encontraríamos con estadísticos más tradicionales. Los árboles de decisión son una técnica estadística para la segmentación, la estratificación, la predicción, la reducción de datos y el filtrado de variables, la identificación de interacciones, la fusión de categorías y la discretización de variables continuas. La función árboles de decisión (Tree) en SPSS crea árboles de clasificación y de decisión para identificar grupos, descubrir las relaciones entre grupos y predecir eventos futuros. Existen diferentes tipos de árbol: CHAID, CHAID exhaustivo, CRT y QUEST, según el que mejor se ajuste a nuestros datos.

Busco información sobre... : estándares para un servicio de búsqueda de información

El servicio de búsqueda de información tiene como finalidad proporcionar respuesta a las preguntas especializadas formuladas por los usuarios de la biblioteca. Los bibliotecarios temáticos consultas fuentes de información y elaboran una respuesta en función de las necesidades del usuario. La Biblioteca Rector Gabriel Ferraté ha elaborado unos estándares de calidad del servicio basados en: A) el análisis de la evolución de diversos indicadores. B) Los atributos de caliudad del servicio des del punto de vista del usuario. C) El análisis de los procesos que lo conforman.