This proposal seeks to characterize the transcriptome and epigenome of genetic models of Alzheimer's
disease (AD) in Drosophila melanogaster. We will use a computational biology approach with a combination of
next-generation sequencing-based techniques to identify both characteristic transcriptional and chromatin state
changes that take place in the brain during AD and further determine how these are modified during both
normal aging and disease progression.
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. This hypothesis is based on several
recent studies linking these processes to neurodegenerative disease in cellular and genetic models. Aim 1 will
explore the transcriptional profile of brain cells in fly AD models. We will use an RNA-seq approach to study
gene expression (both in bulk RNA and at single cell resolution), small RNA species, and transposable
element expression in AD models, and observe how these are affected during aging and disease progression.
Aim 2 will profile the chromatin landscape of brain cells in fly AD models. Specifically, we will determine
chromatin accessibility using ATAC-seq in bulk cells and at single cell resolution. Using the CUT&RUN
technique (similar to ChIP-seq), we will also determine the pattern and abundance of numerous histone marks
relevant to chromatin structure and regulation of gene expression, or implicated in neurodegenerative disease,
including H3K9ac, H3K27ac, H4K16ac, H3K9me2, and H3K36me3. We will also correlate these data sets
together to determine how these AD chromatin profiles and transcriptional programs are affected by aging and
during disease progression.
These experiments will generate rich and comprehensive data sets, analysis of which will yield insights into the
molecular basis of both aging 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.