Relationship between Sleep Loss and Protein Buildup in Alzheimer’s Disease
Detailed Non-Technical Summary
The first aim is to elucidate the interplay of sleep, cognition and proteostasis dysfunction, to provide mechanistic insight on what happens in AD and define early predictors of cognitive decline. Assessments of behavior deficits, EEG changes and proteostasis (single-cell RNA and proteinopathy) will be performed in AD mouse models of pathogenesis to link sleep disruptions with neuronal populations vulnerable to proteinopathy. My second aim is to investigate the therapeutic efficacy of autophagic activation with trehalose to clear proteinopathy and see if it improves sleep and cognition.
My research uses innovative paradigms combining behavior, electrophysiology, gene expression and machine learning, to determine predictors of cognitive decline. I will analyze quiet wakefulness during mouse cognitive tasks, allowing mechanistic linkage of sleep loss and cognitive deficits. Examining the recovery period after sleep deprivation will indicate functional resilience, or lack thereof, which can be correlated to gene expression in the bioinformatic modelling. This will better predict therapeutic efficacy, including lifestyle modifications to improve sleep, or autophagy activation. The overarching goal of my research is to aid development of diagnostic and prognostic biomarkers (i.e., EEG, proteostasis), benefitting from the multimodal experimental approach. Exploring sleep resilience will be critical in understanding how even early changes in vulnerable cells can lead to mounting pathology with age. This will also lead to better understanding of when to intervene and predict therapeutic efficacy, as well as inform on lifestyle-based interventions to delay or prevent AD.