The unique hypusine modification of eIF5A promotes islet β cell inflammation and dysfunction in mice
Bibliographic record
Abstract
In both type 1 and type 2 diabetes, pancreatic islet dysfunction results in part from cytokine-mediated inflammation. The ubiquitous eukaryotic translation initiation factor 5A (eIF5A), which is the only protein to contain the amino acid hypusine, contributes to the production of proinflammatory cytokines. We therefore investigated whether eIF5A participates in the inflammatory cascade leading to islet dysfunction during the development of diabetes. As described herein, we found that eIF5A regulates iNOS levels and that eIF5A depletion as well as the inhibition of hypusination protects against glucose intolerance in inflammatory mouse models of diabetes. We observed that following knockdown of eIF5A expression, mice were resistant to beta cell loss and the development of hyperglycemia in the low-dose streptozotocin model of diabetes. The depletion of eIF5A led to impaired translation of iNOS-encoding mRNA within the islet. A role for the hypusine residue of eIF5A in islet inflammatory responses was suggested by the observation that inhibition of hypusine synthesis reduced translation of iNOS-encoding mRNA in rodent beta cells and human islets and protected mice against the development of glucose intolerance the low-dose streptozotocin model of diabetes. Further analysis revealed that hypusine is required in part for nuclear export of iNOS-encoding mRNA, a process that involved the export protein exportin1. These observations identify the hypusine modification of eIF5A as a potential therapeutic target for preserving islet function under inflammatory conditions.
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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) | 0.000 | 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) | 0.001 | 0.000 |
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".