Genome-wide association studies, or GWAS, have been instrumental in establishing our understanding of Alzheimer’s disease (AD) and the mechanisms that drive it. As technologies improve, new GWAS have revealed new gene variants associated with both risk and resistance of AD. Despite the ever-growing number of gene variants, few of them have been comprehensively investigated to fully understand their impact on AD. This is due to the complexity created by context-specific control of many of these genes. For example, a gene may be critically important and highly expressed in neurons, but not in microglia. This is compounded by the need to account for different tissues and cell subtypes.
Tackling this challenge has been the focus of Dr. Pfenning and his previous CureAlz-funded projects. He specializes in developing techniques that enable him to capture the gene regulatory activity of thousands of gene variants in a single experiment, called massively parallel reporter assays (MPRAs). MPRAs act like a high-speed scanner for the genome, testing thousands of DNA control switches simultaneously to see which specific variants dim, brighten or turn critical genes on or off. His goal in this study is to build on his previous projects by now examining how the gene variants he is investigating respond to the presence of AD pathologies. In addition, he is incorporating new artificial intelligence capacities to improve the interpretability of his assays.
The project consists of two experimental aims. In the first, Dr. Pfenning’s team will perform his MPRA in a mouse model of aggressive amyloid pathology at several time points. This will allow them to compare the impact of different levels of pathology on gene regulatory changes, enabling them to reconstruct progressive changes in the regulation of key risk genes over time and across cell types. In the second aim, Dr. Pfenning’s team will deploy a new spatial reporter assay to investigate how the context of different brain regions impacts gene regulation. As part of this aim, the team will also develop and train a computational program to interpret results from mouse models in the context of other animal models, like rats or non-human primates, and in humans. Developing this program will help identify the most relevant findings from mouse studies for prioritization in further studies in those models or in the development of therapeutic approaches.
