Background
Amyotrophic Lateral Sclerosis (ALS) is a fatal neurodegenerative disease marked by progressive degeneration of upper and lower motor neurons. A defining molecular feature of ALS is the widespread disruption of RNA processing, largely due to the dysfunction of RNA-binding proteins such as TDP-43 and FUS. Loss of TDP-43 function leads to the inclusion of cryptic exons in transcripts, drives transcript degradation and neuronal vulnerability. While short-read RNA sequencing has revealed many splicing defects, its limited read length prevents accurate reconstruction of full-length isoforms and detection of RNA modifications that may influence splicing. Long-read RNA sequencing now enables comprehensive analysis of transcript sequence and modification, offering unprecedented opportunities to study RNA dysregulation in ALS.
Novelty & Importance
This project represents the first systematic investigation of full-length RNA isoforms and modifications across multiple ALS-relevant biological contexts. Integrating long-read, short-read, and direct RNA sequencing, the study will produce an extensive atlas of splicing events in genetically engineered iPSC-derived motor neurons, cell lines, bulk tissues, and post-mortem ALS neurons. The project will leverage in-house and public genetic and transcriptomic data from to uncover splicing quantitative trait loci (sQTLs) and sequence determinants of aberrant splicing. Supervised by Dr Sarah Marzi (neurogenomics and machine learning), Prof. Marc-David Ruepp (RNA biology and cellular models), and Prof. Pietro Fratta (ALS molecular biology and splicing analysis), the project unites cutting-edge computational, genomic, and disease expertise embedded within the DRIVE-Health network.
Aims & Objectives
1) Develop analysis pipelines for alternative splicing and cryptic exon detection across long-read, short-read, and direct RNA datasets.
2) Construct splicing and isoform atlases across multiple cell and tissue sources, in ALS, models and controls.
3) Identify splicing QTLs and sequence determinants of aberrant splicing using statistical and machine-learning models.
4) Characterise RNA modifications and assess their relationship with splicing changes.