Harnessing transcriptomic data to better understand gene regulatory mechanisms of neurodevelopmental disorders

Recent genomic advances have identified over 3,000 genes implicated in neurodevelopmental disorders (NDDs), yet the mechanisms by which their dysregulation impacts brain development remain unclear. Brain development depends on precisely timed, tightly regulated gene expression. This thesis investigates the nonsense-mediated mRNA decay (NMD) pathway —a key post-transcriptional mechanism with critical implications for neurodevelopment and NDDs —using transcriptomic data.. To assess the role of NMD, I first create a novel framework which utilises a data-driven approach to identify in silico NMD targets using steady-state RNA-seq data. I then apply the data-driven NMD (DD-NMD) framework to study NMD roles of the UPF3 paralogs in mouse L-cells by analysing Upf3a- and Upf3b-deficient transcriptomes. These analyses reveal that the loss of Upf3a resembles the loss of UPF3B-NMD and support a mild NMD activator role of UPF3A in mouse L-cells. I then expand my investigations into the role of NMD in the brain by performing a comprehensive transcriptome analysis of Upf1-deficient mouse neuronal cells. NMD dysfunction leads to a broad downregulation of neurodevelopmental genes. I identify several in silico NMD targets which encode epigenetic regulators, including Jarid2. Analysis of publicly available ChIP-seq data reveals that JARID2 target genes are highly enriched among the Upf1-downregulated genes, suggesting a potential interplay between NMD and epigenetic regulation in neuronal cells. Finally, I introduce Brain Integrative Transcriptome Hub (BITHub), a resource which can investigate gene expression patterns of NMD factors, and other NDD genes during brain development. By leveraging nine large-scale publicly available human postmortem brain transcriptomic datasets, I demonstrate how factors such as cell-type composition, RNA degradation, and sequencing metrics can affect and confound gene expression signals in the post-mortem brain. I then examining the temporal and cell-type specific expression of NMD factors which reveals shifts in NMD magnitude during brain development. These analyses deepen our understanding of the NMD pathway and highlight how transcriptomic data can be utilised to provide insights into post-transcriptional regulatory mechanisms.

Author dc:creator

• Nawaz, Urwah

Advisors dc:contributor.advisor

• Gecz, Jozef
• Adelson, David
• Jolly, Lachlan
• Voineagu, Irina (University of New South Wales)

• neurodevelopmental disorders
• transcriptomics
• bioinformatics
• gene regulation

Language dc:language.iso

en

Handle dc:identifier.uri

https://hdl.handle.net/2440/144942

OAI identifier oai:identifier

oai:digital.library.adelaide.edu.au:2440/144942