The amyloid precursor protein (APP) binds the PIKfyve complex and modulates its function

Phosphoinositides are important components of eukaryotic membranes that are required for multiple forms of membrane dynamics. Phosphoinositides are involved in defining membrane identity, mediate cell signalling and control membrane trafficking events. Due to their pivotal role in membrane dynamics, phosphoinositide de-regulation contributes to various human diseases. In this review, we will focus on the newly emerging regulation of the PIKfyve complex, a phosphoinositide kinase that converts the endosomal phosphatidylinositol-3-phosphate [PI(3)P] to phosphatidylinositol-3,5-bisphosphate [PI(3,5)P2)], a low abundance phosphoinositide of outstanding importance for neuronal integrity and function. Loss of PIKfyve function is well known to result in neurodegeneration in both mousemodels and human patients. Our recent work has surprisingly identified the amyloid precursor protein (APP), the central molecule in Alzheimer s disease aetiology, as a novel interaction partner of a subunit of the PIKfyve complex, Vac14. Furthermore, it has been shown that APP modulates PIKfyve function and PI(3,5)P2 dynamics, suggesting that the APP gene family functions as regulator of PI(3,5)P2 metabolism. The recent advances discussed in this review suggest a novel, unexpected, â-amyloid-independent mechanism for neurodegeneration in Alzheimer s disease.

Machine learning for large-scale wearable sensor data in Parkinson disease:concepts, promises, pitfalls, and futures

For the treatment and monitoring of Parkinson's disease (PD) to be scientific, a key requirement is that measurement of disease stages and severity is quantitative, reliable, and repeatable. The last 50 years in PD research have been dominated by qualitative, subjective ratings obtained by human interpretation of the presentation of disease signs and symptoms at clinical visits. More recently, “wearable,” sensor-based, quantitative, objective, and easy-to-use systems for quantifying PD signs for large numbers of participants over extended durations have been developed. This technology has the potential to significantly improve both clinical diagnosis and management in PD and the conduct of clinical studies. However, the large-scale, high-dimensional character of the data captured by these wearable sensors requires sophisticated signal processing and machine-learning algorithms to transform it into scientifically and clinically meaningful information. Such algorithms that “learn” from data have shown remarkable success in making accurate predictions for complex problems in which human skill has been required to date, but they are challenging to evaluate and apply without a basic understanding of the underlying logic on which they are based. This article contains a nontechnical tutorial review of relevant machine-learning algorithms, also describing their limitations and how these can be overcome. It discusses implications of this technology and a practical road map for realizing the full potential of this technology in PD research and practice. © 2016 International Parkinson and Movement Disorder Society.

A spatial pattern analysis of β-amyloid (Aβ) deposition in the temporal lobe in Alzheimer's disease

To determine the factors influencing the distribution of β-amyloid (Aβ) deposits in Alzheimer's disease (AD), the spatial patterns of the diffuse, primitive, and classic Aβ deposits were studied from the superior temporal gyrus (STG) to sector CA4 of the hippocampus in six sporadic cases of the disease. In cortical gyri and in the CA sectors of the hippocampus, the Aβ deposits were distributed either in clusters 200-6400 μm in diameter that were regularly distributed parallel to the tissue boundary or in larger clusters greater than 6400 μm in diameter. In some regions, smaller clusters of Aβ deposits were aggregated into larger 'superclusters'. In many cortical gyri, the density of Aβ deposits was positively correlated with distance below the gyral crest. In the majority of regions, clusters of the diffuse, primitive, and classic deposits were not spatially correlated with each other. In two cases, double immunolabelled to reveal the Aβ deposits and blood vessels, the classic Aβ deposits were clustered around the larger diameter vessels. These results suggest a complex pattern of Aβ deposition in the temporal lobe in sporadic AD. A regular distribution of Aβ deposit clusters may reflect the degeneration of specific cortico-cortical and cortico-hippocampal pathways and the influence of the cerebral blood vessels. Large-scale clustering may reflect the aggregation of deposits in the depths of the sulci and the coalescence of smaller clusters.