This proposal seeks to characterize the transcriptome and epigenome of models of Alzheimer’s disease (AD) in Drosophila melanogaster. The project further proposes to investigate the role of sirtuins in the development and progression of AD, and to understand the molecular mechanism of their reported neuroprotective effects. We will use a computational biology approach with a combination of next-generation sequencing-based techniques to identify both transcriptional and chromatin state changes that take place during AD and determine how these are modified by overexpression or knockout of sirtuin proteins. The specific aims of this project seek to explore the hypothesis that AD is characterized by a loss of heterochromatin, increased genomic instability, and aberrant transcription, and that sirtuin overexpression can stabilize these processes and delay disease progression. This hypothesis is based on several recent studies linking aspects of sirtuin biology to neurodegenerative disease. Aim 1 will determine how modulating sirtuin expression affects the transcriptional profile of fly AD models. We will use an RNA-seq approach to study gene expression, small RNA species, and transposable element expression in AD models, and observe how these are affected upon overexpression or knockout of Sirt6 and Sirt1. Aim 2 will explore how modulating sirtuin expression affects the chromatin landscape of fly AD models. Specifically, we will determine chromatin accessibility using ATAC-seq, and determine the pattern and abundance of histone marks known to be targeted by sirtuins, including H3K9ac, H3K56ac, and H4K16ac by ChIP-seq. We will also determine how these AD chromatin profiles are affected by overexpressing or knocking out sirtuins. These experiments will generate rich and comprehensive data sets, analysis of which will yield insights into both sirtuin biology and Alzheimer’s disease etiology and progression. We also expect to leverage the strengths of the Drosophila model system, including cost, time, and precise control of gene expression, to validate computational biology observations in vivo and follow up with traditional genetic experiments.