<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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".