RNA Splicing Events in Circulation Distinguish Individuals With and Without New-onset Type 1 Diabetes

Webb-Robertson, Bobbie-Jo M.; Wu, Wenting; Flores, Javier E.; Bramer, Lisa M.; Syed, Farooq; Tersey, Sarah A.; May, Sarah C.; Sims, Emily K.; Evans-Molina, Carmella; Mirmira, Raghavendra G.

doi:10.1210/clinem/dgae622

RNA Splicing Events in Circulation Distinguish Individuals With and Without New-onset Type 1 Diabetes

Journal Article · Tue Sep 10 00:00:00 EDT 2024 · Journal of Clinical Endocrinology and Metabolism

DOI:https://doi.org/10.1210/clinem/dgae622· OSTI ID:2557428

Webb-Robertson, Bobbie-Jo M. ^[1]; Wu, Wenting ^[2]; Flores, Javier E. ^[1]; ^[1]; Syed, Farooq ^[2]; Tersey, Sarah A. ^[3]; May, Sarah C. ^[3]; ^[2]; ^[4]; ^[3]

Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
Indiana University School of Medicine, Indianapolis IN (United States)
Univ. of Chicago, IL (United States)
Indiana University School of Medicine, Indianapolis IN (United States); Roudebush Veteran’s Affairs Medical Center, Indianapolis, IN (United States)

Context: Alterations in RNA splicing may influence protein isoform diversity that contributes to or reflects the pathophysiology of certain diseases. Whereas specific RNA splicing events in pancreatic islets have been investigated in models of inflammation in vitro, how RNA splicing in the circulation correlates with or is reflective of type 1 diabetes (T1D) disease pathophysiology in humans remains unexplored. Objective: To use machine learning to investigate if alternative RNA splicing events differ between individuals with and without new-onset T1D and to determine if these splicing events provide insight into T1D pathophysiology. Methods: RNA deep sequencing was performed on whole blood samples from 2 independent cohorts: a training cohort consisting of 12 individuals with new-onset T1D and 12 age- and sex-matched nondiabetic controls and a validation cohort of the same size and demographics. Machine learning analysis was used to identify specific isoforms that could distinguish individuals with T1D from controls. Results: Distinct patterns of RNA splicing differentiated participants with T1D from unaffected controls. Notably, certain splicing events, particularly involving retained introns, showed significant association with T1D. Machine learning analysis using these splicing events as features from the training cohort demonstrated high accuracy in distinguishing between T1D subjects and controls in the validation cohort. Gene Ontology pathway enrichment analysis of the retained intron category showed evidence for a systemic viral response in T1D subjects. Conclusion: Alternative RNA splicing events in whole blood are significantly enriched in individuals with new-onset T1D and can effectively distinguish these individuals from unaffected controls. Further, our findings also suggest that RNA splicing profiles offer the potential to provide insights into disease pathogenesis.

View Accepted Manuscript (DOE)

Research Organization:: Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)

Sponsoring Organization:: National Institutes of Health (NIH); USDOE

Grant/Contract Number:: AC05-76RL01830

OSTI ID:: 2557428

Report Number(s):: PNNL-SA--208201

Journal Information:: Journal of Clinical Endocrinology and Metabolism, Journal Name: Journal of Clinical Endocrinology and Metabolism Journal Issue: 4 Vol. 110; ISSN 0021-972X

Publisher:: Oxford University Press - Endocrine SocietyCopyright Statement

Country of Publication:: United States

Language:: English

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