Pre-mRNA splicing is a critical and regulated processing event where introns are precisely excised from nascent RNA transcripts. As many as one third of all heritable disease mutations result in splicing defects. This grant proposes to identify single nucleotide polymorphisms (SNPs) that confer genetic risk for autoimmune/inflammatory diseases through a mechanism of disrupted splicing. We use a novel high throughput method for identifying allelic differences in protein/RNA binding and splicing. This screen is applied to all SNPs in LD with genetic loci associated with autoimmune or inflammatory diseases. PTB is one of the major regulators of alternate splicing and is mostly highly expressed in the cells of the immune system. This proposal focuses on screening interactions between the basal splicing machinery (including PTB) and polymorphic regions of pre-mRNAs that are associated with autoimmune disorders (i.e. the output of GWAs). The binding of ligand is tested with a novel massively parallel high- throughput binding assay that is an adaptation of the MEGAshift protocol. We then validate these predictions in vivo at their endogenous loci by CLIP and functional assays.
A great deal of research effort has been invested in undertanding how genetic differences between people can contribute to an individual risk of autoimmune disease. While these efforts have identified broad regions which appear important in this process, we have not identified the actual variations that cause risk by disrupting some biological process. This proposal seeks to identify these variations so therapies can be developed to reverse the effect of these variations and improve health outcomes for affected people.
|Lim, Kian Huat; Fairbrother, William Guy (2012) Spliceman--a computational web server that predicts sequence variations in pre-mRNA splicing. Bioinformatics 28:1031-2|
|Ferraris, L; Stewart, A P; Gemberling, M P et al. (2011) High-throughput mapping of protein occupancy identifies functional elements without the restriction of a candidate factor approach. Nucleic Acids Res 39:e33|
|Lim, Kian Huat; Ferraris, Luciana; Filloux, Madeleine E et al. (2011) Using positional distribution to identify splicing elements and predict pre-mRNA processing defects in human genes. Proc Natl Acad Sci U S A 108:11093-8|