Modifying PTEN recruitment promotes neuron survival, regeneration, and functional recovery after CNS injury
Bibliographic record
Abstract
Phosphatase and tensin homolog (PTEN) regulates apoptosis and axonal growth in the developing and adult central nervous system (CNS). Here, we show that human PTEN C-terminal PDZ interactions play a critical role in neuronal apoptosis and axon regeneration after traumatic CNS injury and stroke, highlighted by the findings that antagonizing the PDZ-motif interactions of PTEN has therapeutic applicability for these indications. Interestingly, the death-inducing function of PTEN following ischemic insult depends on a PDZ-domain interaction with MAGI-2 and MAST205, PDZ proteins that are known to recruit PTEN to the plasma membrane and stabilize its interaction with PIP3. Treatments with a human peptide that prevents PTEN association with MAGI-2 or MAST205 increased neuronal survival in multiple stroke models, in vitro. A pro-survival effect was also observed in models of retinal ischemia, optic nerve transection, and after middle cerebral artery occlusion (MCAO) in adult rats. The human PTEN peptide also improved axonal regeneration in the crushed optic nerve. Furthermore, human PTEN peptide therapy promoted functional improvement after MCAO or retinal ischemia induced via ophthalmic artery ligation. These findings show that the human peptide-based targeting of C-terminal PTEN PDZ interactions has therapeutic potential for insults of the CNS, including trauma and stroke.
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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 | 0.000 |
| 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".