O-GlcNAcylation alters the selection of mRNAs for translation and promotes 4E-BP1–dependent mitochondrial dysfunction in the retina
Bibliographic record
Abstract
Diabetes promotes the posttranslational modification of proteins by O- linked addition of GlcNAc ( O- GlcNAcylation) to Ser/Thr residues of proteins and thereby contributes to diabetic complications. In the retina of diabetic mice, the repressor of mRNA translation, eIF4E-binding protein 1 (4E-BP1), is O- GlcNAcylated, and sequestration of the cap-binding protein eukaryotic translation initiation factor (eIF4E) is enhanced. O- GlcNAcylation has also been detected on several eukaryotic translation initiation factors and ribosomal proteins. However, the functional consequence of this modification is unknown. Here, using ribosome profiling, we evaluated the effect of enhanced O- GlcNAcylation on retinal gene expression. Mice receiving thiamet G (TMG), an inhibitor of the O- GlcNAc hydrolase O- GlcNAcase, exhibited enhanced retinal protein O- GlcNAcylation. The principal effect of TMG on retinal gene expression was observed in ribosome-associated mRNAs ( i.e. mRNAs undergoing translation), as less than 1% of mRNAs exhibited changes in abundance. Remarkably, ∼19% of the transcriptome exhibited TMG-induced changes in ribosome occupancy, with 1912 mRNAs having reduced and 1683 mRNAs having increased translational rates. In the retina, the effect of O- GlcNAcase inhibition on translation of specific mitochondrial proteins, including superoxide dismutase 2 (SOD2), depended on 4E-BP1/2. O- GlcNAcylation enhanced cellular respiration and promoted mitochondrial superoxide levels in WT cells, and 4E-BP1/2 deletion prevented O- GlcNAcylation–induced mitochondrial superoxide in cells in culture and in the retina. The retina of diabetic WT mice exhibited increased reactive oxygen species levels, an effect not observed in diabetic 4E-BP1/2–deficient mice. These findings provide evidence for a mechanism whereby diabetes-induced O- GlcNAcylation promotes oxidative stress in the retina by altering the selection of mRNAs for translation.
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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".