<i>Pten</i> Loss Triggers Progressive Photoreceptor Degeneration in an mTORC1-Independent Manner
Bibliographic record
Abstract
Purpose: Silencing Phosphatase and tensin homolog (Pten) is a proposed therapeutic strategy for tissue regeneration to treat neurological disorders. However, Pten is pleiotropic, inhibiting several signaling and metabolic pathways, including mTORC1 and glycolysis, both pro-regenerative in certain contexts. This study aims to assess the long-term impact of inactivating Pten on photoreceptor survival in the retina and to identify downstream pathway(s). Methods: We assessed retinal integrity in Pten conditional knock-outs (cKOs) that were retinal progenitor cell (RPC)-specific (Pten RPC-cKO), a congenital model, or rod-specific (Pten Rho-cKO). We examined early changes in photoreceptor gene expression and used immunostaining to assess photoreceptors, reactive astrocytes, microglia, angiogenesis, and subretinal deposit formation from postnatal day (P) 21 to 1 year of age. Pten RPC-cKO retinal explants were treated with rapamycin, an mTOR inhibitor, or 2-deoxy-D-glucose (2DG), a glycolysis inhibitor. Results: In both Pten-cKO models, retinas display signs of early pathogenesis as photoreceptor-specific gene expression is downregulated at P0, before photoreceptor loss. Pten loss triggers progressive rod and cone degeneration beginning at P21 in Pten RPC-cKOs and at 6 months of age in Pten Rho-cKOs. Activated microglia and astrocytes, and increased angiogenesis, are observed in both Pten-cKO models, while subretinal amyloid-β deposits develop in Pten RPC-cKOs. Rapamycin accelerates photoreceptor degeneration in Pten RPC-cKOs, whereas 2DG has no effect. Conclusions: Our findings suggest that Pten loss, either in RPCs as a congenital model, or solely in mature rod photoreceptors, leads to progressive retinal degeneration that is exacerbated by mTORC1 suppression, drawing into question the therapeutic value of Pten-mTORC1 manipulations.
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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 | 0.001 |
| 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".