The Effects of Molecular Chaperones on Tau Fibril Assembly

The accumulation of microtubule-associated protein tau into fibrillar aggregates is the hallmark of Alzheimer’s disease and other neurodegenerative disorders, collectively referred to as tauopathies. Fibrils can propagate from one cell to the next and spread throughout the brain. However, a study shows that only small aggregates can be taken up by cultured neuronal cells. The mechanisms that lead to the breakage of fibrils into smaller fragments remain unknown. In yeast, the AAA+ chaperone HSP104 processes the reactivation of protein aggregates and is responsible for fragmentation of fibrils. This study focused on investigating the effects of molecular chaperones on tau fibrils and using HSP104 as a model system to test whether we can monitor fibril fracturing. The assays used to detect the chaperone’s actions on tau utilized acrylodan fluorescence, thioflavin T fluorescence, and sedimentation. Tau fibrils were either formed with a cofactor, heparin, to accelerate assembly or without a cofactor. In the process of investigating the effects of HSP104 on tau fibrils, this study established an assay to determine the effects of breakage on the seeding properties of tau fibrils. Our findings demonstrated that the sonication of tau fibrils produces smaller fragments (seeds) that accelerate the conversion of monomeric tau into fibrils. The use of this assay with HSP104 provided evidence that HSP104 inhibits the elongation of tau fibrils. Indeed, HSP104 inhibits the aggregation of soluble tau into aggregates. However, tau fibril breakage and dissociation were not observed with HSP104, either alone or in combination with co-chaperones (HSP70 and HSP40). Our findings provide insights into the seeding properties of tau fibrils, and suggest that fragmentation is a critical part of tau assembly. This knowledge should be valuable for understanding tau fibril aggregation and propagation in the brain, which is necessary to identify new treatments for neurodegenerative diseases.

Identification of geostationary satellites using polarization data from unresolved images

In order to protect critical military and commercial space assets, the United States Space Surveillance Network must have the ability to positively identify and characterize all space objects. Unfortunately, positive identification and characterization of space objects is a manual and labor intensive process today since even large telescopes cannot provide resolved images of most space objects. Since resolved images of geosynchronous satellites are not technically feasible with current technology, another method of distinguishing space objects was explored that exploits the polarization signature from unresolved images. The objective of this study was to collect and analyze visible-spectrum polarization data from unresolved images of geosynchronous satellites taken over various solar phase angles. Different collection geometries were used to evaluate the polarization contribution of solar arrays, thermal control materials, antennas, and the satellite bus as the solar phase angle changed. Since materials on space objects age due to the space environment, it was postulated that their polarization signature may change enough to allow discrimination of identical satellites launched at different times. The instrumentation used in this experiment was a United States Air Force Academy (USAFA) Department of Physics system that consists of a 20-inch Ritchey-Chrétien telescope and a dual focal plane optical train fed with a polarizing beam splitter. A rigorous calibration of the system was performed that included corrections for pixel bias, dark current, and response. Additionally, the two channel polarimeter was calibrated by experimentally determining the Mueller matrix for the system and relating image intensity at the two cameras to Stokes parameters S0 and S1. After the system calibration, polarization data was collected during three nights on eight geosynchronous satellites built by various manufacturers and launched several years apart. Three pairs of the eight satellites were identical buses to determine if identical buses could be correctly differentiated. When Stokes parameters were plotted against time and solar phase angle, the data indicates that there were distinguishing features in S0 (total intensity) and S1 (linear polarization) that may lead to positive identification or classification of each satellite.