DNA synthesis and neuronal apoptosis caused by familial Alzheimer disease mutants of the amyloid precursor protein are mediated by the p21 activated kinase PAK3.

Apoptotic pathways and DNA synthesis are activated in neurons in the brains of individuals with Alzheimer disease (AD). However, the signaling mechanisms that mediate these events have not been defined. We show that expression of familial AD (FAD) mutants of the amyloid precursor protein (APP) in primary neurons in culture causes apoptosis and DNA synthesis. Both the apoptosis and the DNA synthesis are mediated by the p21 activated kinase PAK3, a serine-threonine kinase that interacts with APP. A dominant-negative kinase mutant of PAK3 inhibits the neuronal apoptosis and DNA synthesis; this effect is abolished by deletion of the PAK3 APP-binding domain or by coexpression of a peptide representing this binding domain. The involvement of PAK3 specifically in FAD APP-mediated apoptosis rather than in general apoptotic pathways is suggested by the facts that a dominant-positive mutant of PAK3 does not alone cause neuronal apoptosis and that the dominant-negative mutant of PAK3 does not inhibit chemically induced apoptosis. Pertussis toxin, which inactivates the heterotrimeric G-proteins Go and Gi, inhibits the apoptosis and DNA synthesis caused by FAD APP mutants; the apoptosis and DNA synthesis are rescued by coexpression of a pertussis toxin-insensitive Go. FAD APP-mediated DNA synthesis precedes FAD APP-mediated apoptosis in neurons, and inhibition of neuronal entry into the cell cycle inhibits the apoptosis. These data suggest that a normal signaling pathway mediated by the interaction of APP, PAK3, and Go is constitutively activated in neurons by FAD mutations in APP and that this activation causes cell cycle entry and consequent apoptosis.

tranSMART: An Open Source Knowledge Management and High Content Data Analytics Platform.

The tranSMART knowledge management and high-content analysis platform is a flexible software framework featuring novel research capabilities. It enables analysis of integrated data for the purposes of hypothesis generation, hypothesis validation, and cohort discovery in translational research. tranSMART bridges the prolific world of basic science and clinical practice data at the point of care by merging multiple types of data from disparate sources into a common environment. The application supports data harmonization and integration with analytical pipelines. The application code was released into the open source community in January 2012, with 32 instances in operation. tranSMART's extensible data model and corresponding data integration processes, rapid data analysis features, and open source nature make it an indispensable tool in translational or clinical research.

Detection of activity centers in cellular pathways using transcript profiling.

We present a new computational method for identifying regulated pathway components in transcript profiling (TP) experiments by evaluating transcriptional activity in the context of known biological pathways. We construct a graph representing thousands of protein functional relationships by integrating knowledge from public databases and review articles. We use the notion of distance in a graph to define pathway neighborhoods. The pathways perturbed in an experiment are then identified as the subgraph induced by the genes, referred to as activity centers, having significant density of transcriptional activity in their functional neighborhoods. We illustrate the predictive power of this approach by performing and analyzing an experiment of TP53 overexpression in NCI-H125 cells. The detected activity centers are in agreement with the known TP53 activation effects and our independent experimental results. We also apply the method to a serum starvation experiment using HEY cells and investigate the predicted activity of the transcription factor MYC. Finally, we discuss interesting properties of the activity center approach and its possible applications beyond the comparison of two experiments.