The role of amyloid precursor protein in neuronal and non-neuronal cell lines

Models of Alzheimer’s disease (AD) have provided useful insights into the pathogenesis and mechanistic pathways that lead to its development. One emerging idea about AD is that it may be described as a hypometabolic disorder due to the reduction of glucose uptake in AD brains. Inappropriate processing of Amyloid Precursor Protein (APP) is considered central to the initiation and progression of the disease. Although the exact role of APP misprocessing is unclear, it may play a role in neuronal metabolism before the onset of neurodegeneration. To investigate the potential role of APP in neuronal metabolism, the SHSY5Y neuroblastoma cell line was used to generate cell lines that stably overexpress wild type APP695 or express Swedish mutated-APP observed in familial AD (FAD), both under the control of the neuronal promoter, Synapsin I. The effects of APP on glucose uptake, cellular stress and energy homeostasis were studied extensively. It was found that APP-overexpressing cells exhibited decreased glucose uptake with changes in basal oxygen consumption in comparison to control cell lines. Similar studies were also performed in fibroblasts taken from FAD patients compared with control fibroblasts. Previous studies found FAD-derived fibroblasts displayed altered metabolic profiles, calcium homeostasis and oxidative stress when compared to controls. As such, in this study fibroblasts were studied in terms of their ability to metabolise glucose and their mitochondrial function. Results show that FAD-derived fibroblasts demonstrate no differences in mitochondrial function, or response to oxidative stress compared to control fibroblasts. However, control fibroblasts treated with Aβ1-42 demonstrated changes in glucose uptake. This study highlights the importance of APP expression within non-neuronal cell lines, suggesting that whilst AD is considered a brain-associated disorder, peripheral effects in non-neuronal cell types should also be considered when studying the effects of Aβ on metabolism.

Stabilization of catalyst particles against sintering on oxide supports with high oxygen ion lability exemplified by Ir-catalyzed decomposition of N2O

Iridium nanoparticles deposited on a variety of surfaces exhibited thermal sintering characteristics that were very strongly correlated with the lability of lattice oxygen in the supporting oxide materials. Specifically, the higher the lability of oxygen ions in the support, the greater the resistance of the nanoparticles to sintering in an oxidative environment. Thus with γ-Al2O3 as the support, rapid and extensive sintering occurred. In striking contrast, when supported on gadolinia-ceria and alumina-ceria-zirconia composite, the Ir nanoparticles underwent negligible sintering. In keeping with this trend, the behavior found with yttria-stabilized zirconia was an intermediate between the two extremes. This resistance, or lack of resistance, to sintering is considered in terms of oxygen spillover from support to nanoparticles and discussed with respect to the alternative mechanisms of Ostwald ripening versus nanoparticle diffusion. Activity towards the decomposition of N2O, a reaction that displays pronounced sensitivity to catalyst particle size (large particles more active than small particles), was used to confirm that catalytic behavior was consistent with the independently measured sintering characteristics. It was found that the nanoparticle active phase was Ir oxide, which is metallic, possibly present as a capping layer. Moreover, observed turnover frequencies indicated that catalyst-support interactions were important in the cases of the sinter-resistant systems, an effect that may itself be linked to the phenomena that gave rise to materials with a strong resistance to nanoparticle sintering.

TATPred:a Bayesian method for the identification of twin arginine translocation pathway signal sequences

The twin arginine translocation (TAT) system ferries folded proteins across the bacterial membrane. Proteins are directed into this system by the TAT signal peptide present at the amino terminus of the precursor protein, which contains the twin arginine residues that give the system its name. There are currently only two computational methods for the prediction of TAT translocated proteins from sequence. Both methods have limitations that make the creation of a new algorithm for TAT-translocated protein prediction desirable. We have developed TATPred, a new sequence-model method, based on a Nave-Bayesian network, for the prediction of TAT signal peptides. In this approach, a comprehensive range of models was tested to identify the most reliable and robust predictor. The best model comprised 12 residues: three residues prior to the twin arginines and the seven residues that follow them. We found a prediction sensitivity of 0.979 and a specificity of 0.942.