Relationship Between Sleep Loss and Protein Buildup in Alzheimer’s Disease

My project examines how protein turnover/proteostasis and sleep changes impact cognition and Alzheimer’s disease (AD) and if we can reverse the effects therapeutically.

I hypothesize that sleep loss and protein recycling failure are interactive events in Alzheimer’s disease, that precede memory loss and predict disease progression. To test this, I will look at sleep and memory, neuronal function, and markers of protein recycling and pathology in an Alzheimer’s disease mouse model to see if improving protein recycling can rescue behavior. Artificial intelligence models will be used to assess the contribution of these biological events to predict memory loss and disease risk, facilitating the discovery of novel biomarkers and treatments for Alzheimer’s disease.

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.