Proinflammatory cytokines suppress nonsense-mediated RNA decay to impair regulated transcript isoform processing in pancreatic β cells
Bibliographic record
Abstract
Introduction: Proinflammatory cytokines are implicated in pancreatic ß cell failure in type 1 and type 2 diabetes and are known to stimulate alternative RNA splicing and the expression of nonsense-mediated RNA decay (NMD) components. Here, we investigate whether cytokines regulate NMD activity and identify transcript isoforms targeted in ß cells. Methods: A luciferase-based NMD reporter transiently expressed in rat INS1(832/13), human-derived EndoC-ßH3, or dispersed human islet cells is used to examine the effect of proinflammatory cytokines (Cyt) on NMD activity. The gain- or loss-of-function of two key NMD components, UPF3B and UPF2, is used to reveal the effect of cytokines on cell viability and function. RNA-sequencing and siRNA-mediated silencing are deployed using standard techniques. Results: Cyt attenuate NMD activity in insulin-producing cell lines and primary human ß cells. These effects are found to involve ER stress and are associated with the downregulation of UPF3B. Increases or decreases in NMD activity achieved by UPF3B overexpression (OE) or UPF2 silencing raise or lower Cyt-induced cell death, respectively, in EndoC-ßH3 cells and are associated with decreased or increased insulin content, respectively. No effects of these manipulations are observed on glucose-stimulated insulin secretion. Transcriptomic analysis reveals that Cyt increases alternative splicing (AS)-induced exon skipping in the transcript isoforms, and this is potentiated by UPF2 silencing. Gene enrichment analysis identifies transcripts regulated by UPF2 silencing whose proteins are localized and/or functional in the extracellular matrix (ECM), including the serine protease inhibitor SERPINA1/α-1-antitrypsin, whose silencing sensitizes ß-cells to Cyt cytotoxicity. Cytokines suppress NMD activity via UPR signaling, potentially serving as a protective response against Cyt-induced NMD component expression. Conclusion: Our findings highlight the central importance of RNA turnover in ß cell responses to inflammatory stress.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".