REDD2-mediated inhibition of mTOR promotes dendrite retraction induced by axonal injury
Bibliographic record
Abstract
Dendritic defects occur in neurodegenerative diseases accompanied by axonopathy, yet the mechanisms that regulate these pathologic changes are poorly understood. Using Thy1-YFPH mice subjected to optic nerve axotomy, we demonstrate early retraction of retinal ganglion cell (RGC) dendrites and selective loss of mammalian target of rapamycin (mTOR) activity, which precede soma loss. Axonal injury triggered rapid upregulation of the stress-induced protein REDD2 (regulated in development and DNA damage response 2), a potent inhibitor of mTOR. Short interfering RNA-mediated REDD2 knockdown restored mTOR activity and rescued dendritic length, area and branch complexity in a rapamycin-dependent manner. Whole-cell recordings demonstrated that REDD2 depletion leading to mTOR activation in RGCs restored their light response properties. Lastly, we show that REDD2-dependent mTOR activity extended RGC survival following axonal damage. These results indicate that injury-induced stress leads to REDD2 upregulation, mTOR inhibition and dendrite pathology causing neuronal dysfunction and subsequent cell death.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | high |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | high |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".