Alternative splicing emerges as a mechanistic focus in type 1 diabetes risk research, with the University of Virginia receiving USD 1.44 million from NIDDK to investigate how RNA processing errors in immune cells may drive the transition from genetic predisposition to islet autoimmunity.
University of Virginia has received a USD 1.44 million NIH R01 grant from the National Institute of Diabetes and Digestive and Kidney Diseases to examine alternative splicing as a mechanistic driver of islet autoimmunity in the TEDDY longitudinal cohort. The award, issued in April 2026, funds an ancillary study led by Suna Onengut-Gumuscu, alongside co-investigators Aakrosh Ratan and Stephen S. Rich.
The project targets a gap in understanding how non-coding genetic variants — which account for the majority of T1D-associated loci — translate into immune dysregulation. Prior work from the team identified associations between alternative splicing events and known T1D risk loci using short-read RNA sequencing. The current award extends that work by applying both long-read and short-read RNA sequencing to CD4+ T cells collected from TEDDY participants at multiple time points, enabling full isoform detection alongside quantification of isoform abundance. Twelve genes within established T1D loci that encode RNA-binding proteins have been identified as candidates, with the project aiming to map how aberrant splicing regulated by these proteins shapes autoimmune progression over time. Deep learning time-series models will be used to integrate the transcriptomic data with longitudinal clinical and biomarker profiles, with the goal of improving prediction of islet autoimmunity onset and T1D risk trajectory.
Cohort context and translational relevance
TEDDY — the Environmental Determinants of Diabetes in the Young study — is a prospective cohort that has followed newborns at high genetic risk for T1D since infancy, with repeated sampling and deep phenotyping. Its infrastructure makes it well suited for the kind of longitudinal molecular profiling this project requires. By anchoring the splicing analysis to defined transition periods in islet autoimmunity, the investigators aim to identify not only mechanistic targets but also potential windows for monitoring and intervention. The translational intent is explicit: the grant abstract cites improved risk prediction and novel therapeutic targets as intended outcomes, though those applications remain preclinical at this stage.