Evaluation of neuroinflammation as modulator of tau aggregation in response to repetitive mild traumatic brain injury

The pathological misfolding and aggregation of hyper-phosphorylated tau (ptau) protein in neurofibrillary tangles (NFTs) is the main hallmark of a group of neurodegenerative diseases called tauopathies. These include important disorders such as Alzheimer’s Disease (AD) and chronic traumatic encephalopathy (CTE). Mounting evidence supports that the aggregation and deposition of misfolded proteins is an early event in the development of AD and CTE. Several studies uphold traumatic brain injury (TBI) as an important risk factor for both disorders, since there is a relationship between TBI severity and frequency, and the vulnerability to develop dementia. Importantly, individuals affected by TBI show elevated levels of ptau in cerebrospinal fluid and NFTs in specific brain areas. Although previous studies on tau mice subjected to TBI show increased ptau burden, the mechanisms underlying this phenomenon have not been explored. Besides, previous research neither recapitulate the effects of repetitive mild TBI (rmTBI), reported to lead to long-lasting neurological consequences. Acute and chronic inflammation are intimately associated to TBI events, but its role on tau aggregation over time remains unknown. Here, we analyze the neuroinflammatory response, at both short- and long-term, in transgenic tau mice after rmTBI. Our data suggest that rmTBI triggers microglia and astroglia activation and that they may be involved in the increase observed on tau pathology in concussed mice. This could indicate that neuroinflammation could have an active role in the increased risk to develop tauopathies after brain concussion.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Late-life depression is able to accelerate learning and memory impairment in a mouse model of Alzheimer´s disease

Clinical studies suggest that depression could be considered an important risk factor for the future development of cognitive impairment and Alzheimer's disease (AD). In fact, there is a strong association between late-life depression and AD. The age of AD onset has been shown to be accelerated in patients with mild cognitive impairment (MCI) with a history of depression, and women appear to be particularly more vulnerable to this condition. In addition, individuals with MCI who present depressive symptoms have an elevated burden of amyloid-beta (Aβ), the main toxic protein associated with Alzheimer's pathology, and a higher risk of developing AD compared to non-depressed MCI patients. Although it has been described that some transgenic models of AD can develop signs similar to depression in advanced stages, the induction of Alzheimer's pathology due to a depressive process has not been studied under experimental conditions to emulate late-life depression as a risk factor for AD. The objective of this study is to determine, by inducing unpredictable mild chronic stress (CUMS) in tau transgenic P301S mice, whether depression is a cause, rather than a consequence, of AD development. The results of our study indicate that the induction of CUMS in transgenic animals induces phenotypic changes related to a depressive state. Behavioral and histological studies suggest that depression-like induction can worse AD pathology. The findings generated in this project could provide evidence of depression as a risk factor for AD

18F-THK5351 PET imaging, neuropathology and clinical progression in a tau mouse model

Aims: Alzheimer’s disease (AD) and other associated dementias remain a consistent and unruly problem for the aging population and health. The neuropathology of AD is characterized by the extracellular deposition of beta-amyloid protein (Aβ) and the formation of intraneuronal neurofibrillary tangles (NFT) composed of hyperphosphorylated tau (ptau), along with neuroinflammation and neuronal loss that ultimately induces to noticeable cognitive impairments. Abnormal ptau leads to the formation of insoluble, beta-sheet rich amyloid aggregates in tauopathies such as AD. Positron emission tomography (PET) imaging is a promising avenue that may identify tau aggregates in vivo cross-sectionally and longitudinally in various dementia conditions. Methods: The goal of this study is to characterize the longitudinal assessment of the tau tracer 18F-THK5351 by in vivo tau PET imaging concomitantly to behavior and tau pathology by histology and biochemistry from 6 to 12 months of age in tau transgenic P301S mice, a mouse model of tauopathies. Results: Our results demonstrate an augmentation of overall gross brain tau pathology by in vivo PET imaging in P301S mice compared to age-matched wild-type (WT) animals accompanied by P301S-model associated pathological tau and phenotypic and behavioral deficits. Conclusions: This longitudinal study provides new insights on the relationship between imaging diagnostic tools, the in vivo neuropathological temporal pattern and the clinical signs observed in animal models of AD that could benefit early disease diagnosis.This work was partially funded by Department of Defense Peer Reviewed Alzheimer’s Research Program Convergence Science. Research Award grant AZ160106 and Alzheimer’s Association New Investigator Research Grant NIRG-394284 to IMG. Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Extraction of the Muon Signals Recorded with the Surface Detector of the Pierre Auger Observatory Using Recurrent Neural Networks

We present a method based on the use of Recurrent Neural Networks to extract the muon component from the time traces registered with water-Cherenkov detector (WCD) stations of the Surface Detector of the Pierre Auger Observatory. The design of the WCDs does not allow to separate the contribution of muons to the time traces obtained from the WCDs from those of photons, electrons and positrons for all events. Separating the muon and electromagnetic components is crucial for the determination of the nature of the primary cosmic rays and properties of the hadronic interactions at ultra-high energies. We trained a neural network to extract the muon and the electromagnetic components from the WCD traces using a large set of simulated air showers, with around 450 000 simulated events. For training and evaluating the performance of the neural network, simulated events with energies between 1018.5, eV and 1020 eV and zenith angles below 60 degrees were used. We also study the performance of this method on experimental data of the Pierre Auger Observatory and show that our predicted muon lateral distributions agree with the parameterizations obtained by the AGASA collaboration