The effects of Alzheimer's disease risk genes on metabolism and signaling across cell types

Summary Alzheimer's disease (AD) is pervasive and debilitating, with no truly effective treatments. Genome wide association studies have found risk variants for sporadic, late-onset AD, but the mechanisms driving this risk are still unknown. Two of the sAD variants with the highest association with development of AD are in Apolipoprotein E (APOE) and ATP-binding cassette transporter A7 (ABCA7), both of which are involved in lipid metabolism. Our prior work demonstrates that the E4 allele of APOE (APOE4) has cell type specific effects, including alterations in lipid metabolism, but important questions remain about the downstream pathways affected by this allele. Critically, we do not know how APOE4-induced changes interact with aging-related stress, leading to late-onset disease. Even less is known about how ABCA7 alleles lead to increased risk of AD. We propose to use a systems biology approach to discover these AD-risk pathways, responding to NOT-AG-18-052 from the NIH, which designates “systems biology of brain neural cells derived from human AD induced pluripotent stem cells” as a high-priority research topic. Our approach uses multi-omic analysis of induced pluripotent stem cell (iPSC) lines that are isogenic for two risk variants, APOE4 or ABCA7 premature termination (PTC), which can then be differentiated into diverse cell types. Using an unbiased approach, we will reveal how AD-risk alleles alter signaling, metabolism, and states of the cells, how they affect individual cells as well as cell-cell interactions in complex cultures, and how they alter cellular responses to acute stress. In Aim 1 we will deeply characterize the effects of APOE4 and ABCA7 PTC in 2D culture models of neurons, astrocytes, microglia and pericytes, differentiated from isogenic iPSC lines, examining changes in metabolism and post-translational modifications (PTMs) of proteins. We will use advanced network optimization methods to integrate the disparate data and to uncover molecular interaction networks that link together changes observed in the individual omics. In Aim 2, we investigate the pathways altered by risk alleles that influence cell-cell interactions in 3D culture models, using spatially-resolved PTM- proteomics and metabolomics/lipidomics and causal computational models. In Aim 3, we will examine the intersection of risk variant with environmental and cellular stressors in the 3D culture models. Each aim includes rigorous testing of hypotheses in vitro and by examination of postmortem samples.