Autism Spectrum Disorder (ASD) is a prevalent neurodevelopmental disorder characterized by deficits in social communication skills and repetitive or restrictive behaviors. ASD is highly heritable, however its underlying genetic architecture is complex, with hundreds of implicated genes. Recently, we and others have identified a common signature of gene expression alterations in post-mortem ASD cerebral cortex, demonstrating a convergence at the level of transcriptional regulation. To better understand ASD disease mechanisms and develop effective clinical therapeutics, it is essential to identify critical drivers of the convergent ASD gene expression signature. In this project, I will integrate multiple molecular datasets: genetic, transcriptomic, and epigenomic profiling in ASD brain tissues to identify genes driving transcriptional changes in ASD.
In Aim 1, I will utilize genetic and epigenomic data to identify upstream regulators of transcriptional changes in ASD.
In Aim 2, I will utilize network methods to combine genetic, transcriptomic, and epigenomic information together in order to identify subtypes of ASD patients that share common molecular patterns. I will then characterize gene expression changes within each subtype and identify subtype-specific genetic or epigenetic drivers.
In Aim 3, as a key proof of principle, I will validate candidate regulators by knockdown and overexpression in primary human fetal neural progenitor cells in vitro followed by regulatory network analysis. This project will be one of the first to integrate multiple molecular datasets from a primary tissue to characterize disease pathways and identify critical driver genes in a neurodevelopmental disorder. Validated driver genes are likely to be important transcriptional regulators during brain development and potential targets for clinical therapeutics in ASD.
Autism Spectrum Disorder (ASD) is prevalent neurodevelopmental disorder characterized by impairments in social communication and restricted or repetitive behaviors. The molecular mechanisms underlying ASD are many, and not well understood. This proposal will integrate diverse molecular datasets to identify critical genes driving dysfunctions in ASD brains, improving our understanding of the disease and contributing to the identification of potential therapeutic targets.
