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Cerebral ischemia-reperfusion (I/R) injury is a major problem worldwide. The Notch signaling pathway plays an important role in neural progenitor cell differentiation and in the inflammatory response after central nervous system injury. This study evaluated whether the neuroprotective effect of cerebral ischemic preconditioning (cIPC) is mediated by the preactivation of the Notch signaling pathway. A rat middle cerebral artery occlusion/reperfusion (MCAO/R) model and glucose deprivation/reoxygenation (OGD/R) cell model were constructed to detect the neuroprotective effects of cIPC. In in vivo experiments, cIPC reduces the neurological functional deficit, cerebral infarction, and cellular apoptosis in the hippocampus induced by middle cerebral artery occlusion/reperfusion (MCAO/R), thus indicating that cIPC can improve neurologic function. Moreover, cIPC can reveal the expression peak of Jagged1, Notch1, NICD, and Hes1 protein, thereby indicating that cIPC can preactivate Notch signaling. However, cIPC-induced improvements in neurologic function are compromised by the γ-secretase inhibitor N-(N-(3,5-difluorophenacetyl)-1-alanyl)-S-phenylglycine t-butyl ester (DAPT). In in vitro experiments, OGD preconditioning (OGDPC) can clearly upregulate Notch1 expression in the OGD/R-treated neuron and neural stem cell. Notch1 pre-overexpression can decrease neuron death and apoptosis under OGD/R treatment. Notch1 pre-overexpression can decrease the percentage of G1 stage cells and increase the percentage of S stage cells in OGD/R-treated neural stem cell. Furthermore, Notch1 pre-knockdown has the opposite effect on cell survival, apoptosis, and cycle in both OGD/R-treated neuron and neural stem cell. In conclusion, our results demonstrate that the neuroprotective effects of cIPC in a rat MCAO/R model are mediated by the preactivation of the Notch signaling pathway. [ABSTRACT FROM AUTHOR]

Apoptosis proteins play an important role in the mechanism of programmed cell death. Predicting subcellular localization of apoptosis proteins is an essential step to understand their functions and identify drugs target. Many computational prediction methods have been developed for apoptosis protein subcellular localization. However, these existing works only focus on the proteins that have one location; proteins with multiple locations are either not considered or assumed as not existing when constructing prediction models, so that they cannot completely predict all the locations of the apoptosis proteins with multiple locations. To address this problem, this paper proposes a novel multilabel predictor named MultiP-Apo, which can predict not only apoptosis proteins with single subcellular location but also those with multiple subcellular locations. Specifically, given a query protein, GO-based feature extraction method is used to extract its feature vector. Subsequently, the GO feature vector is classified by a new multilabel classifier based on the label-specific features. It is the first multilabel predictor ever established for identifying subcellular locations of multilocation apoptosis proteins. As an initial study, MultiP-Apo achieves an overall accuracy of 58.49% by jackknife test, which indicates that our proposed predictor may become a very useful high-throughput tool in this area. [ABSTRACT FROM AUTHOR]

Apoptosis proteins play a key role in maintaining the stability of organism; the functions of apoptosis proteins are related to their subcellular locations which are used to understand the mechanism of programmed cell death. In this paper, we utilize GO annotation information of apoptosis proteins and their homologous proteins retrieved from GOA database to formulate feature vectors and then combine the distance weighted KNN classification algorithm with them to solve the data imbalance problem existing in CL317 data set to predict subcellular locations of apoptosis proteins. It is found that the number of homologous proteins can affect the overall prediction accuracy. Under the optimal number of homologous proteins, the overall prediction accuracy of our method on CL317 data set reaches 96.8% by Jackknife test. Compared with other existing methods, it shows that our proposed method is very effective and better than others for predicting subcellular localization of apoptosis proteins. [ABSTRACT FROM AUTHOR]