Developing a molecular network map of GABAergic neuron subtypes from single nucleus data to identify drivers of Alzheimer's disease and late-life depression

Alzheimer's disease (AD) is a complex neurodegenerative disorder, affecting 35.6 million people worldwide in 2010 and projected to double in prevalence every 20 years, with a prevalence of 115.4 million by 2050. Late-life depression (LLD; onset greater than 60 years) increases the risk for AD due to a high-degree of symptomatic overlap emanating from common biological vulnerabilities that contribute to accelerated ageing. Within these vulnerable networks, GABA is a primary inhibitory neurotransmitter that regulates molecular systems related to cognition and depressive symptoms. However, the contribution of pathways modifying the overlapping biology of AD and LLD remains unclear. Advances in gene expression technologies, such as single-nucleus gene expression, have revealed the importance of different cell types in contributing to disease symptoms. Therefore, the goal of our proposal is to explore the gene expression profiles of GABA-related cells in the brain tissue of older adults with AD and LLD. Our specific aims will include: (I) Identifying genes that show similar patterns of expression within different types of GABA-related brain cells; (II) Exploring gene expression patterns to identify proteins that may be potential drug targets for AD and LLD; (III) Exploring how individual genetic variation in DNA can be used to predict gene expression patterns; and, (IV) Building models to understand which gene expression patterns are the most important in predicting AD and LLD. For these aims, we will make use of 900 individuals from two longitudinal studies of ageing: The Religious Orders Study (ROS) and the Memory and Aging Project (MAP) cohorts. Using these established methods from genetics and computational biology, we will explore GABA-related brain cells to understand the risk for AD and LLD, revealing corresponding new drug targets.