Bayesian network inference modeling identifies TRIB1 as a novel regulator of cell cycle progression and survival in cancer cells
Menée notamment à l'aide d'outils de modélisation par réseaux bayésiens sur des données de lignées cellulaires, ainsi qu'à l'aide de données issues de l'analyse d'échantillons de tumeurs du sein, cette étude identifie le rôle joué par le gène TRIB1 dans la survie des cellules cancéreuses
Molecular networks governing cellular responses to targeted therapies are complex dynamic systems with non-intuitive behaviors. Here we applied a novel computational strategy to infer probabilistic causal relationships between network components based on gene expression. We constructed a model comprised of an ensemble of networks using multidimensional data from cell line models of cell cycle arrest caused by inhibition of MEK1/2. Through simulation of reverse-engineered Bayesian network modeling, we generated predictions of G1-S transition. The model identified known components of the cell cycle machinery, such as CCND1, CCNE2 and CDC25A, as well as novel regulators of G1-S transition IER2, TRIB1 and TRIM27. Experimental validation of this model confirmed 10 of 12 predicted genes to have a role in progression through the G1-S phase transition of the cell cycle. Further analysis showed that TRIB1 regulated the cyclin D1 promoter via NF-κB and AP-1 sites and sensitized cells to TRAIL-induced apoptosis. In clinical specimens of breast cancer, TRIB1 levels correlated with expression of NF-κB and its target genes IL-8 and CSF2, and TRIB1 copy number and expression were predictive of clinical outcome. Together, our results establish a critical role for TRIB1 in cell cycle and survival that is mediated via the modulation of NF-κB signaling.