Preservation of GABA<sub>A</sub>Receptor Function by PTEN Inhibition Protects Against Neuronal Death in Ischemic Stroke
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Downregulation of the tumor suppressor, phosphatase and tensin homolog deleted on chromosome 10 (PTEN), is thought to be a novel neuroprotective strategy in ischemic stroke, but the underlying mechanisms remain unclear. In this study, we aimed to validate the use of PTEN regulation of gamma-aminobutyric acid subtype A receptors (GABA(A)Rs) as a molecular target for the treatment of ischemic stroke. Because suppression of GABA(A)Rs contributes to ischemic neuron death, describing the intracellular signaling that interacts with GABA(A)Rs in ischemic neurons would provide a molecular basis for novel stroke therapies. METHODS: We measured surface GABA(A)R expression by immunocytochemical labeling and surface protein biotinylation assay. Knockdown and overexpression approaches were used to test the effects of PTEN on the expression and function of GABA(A)Rs. Neuronal death was detected in both in vitro and in vivo stroke models. RESULTS: The knockdown and overexpression approaches provided the first evidence that PTEN negatively regulated membrane expression and function of GABA(A)Rs in rat hippocampal neurons. Importantly, we demonstrated that a PTEN inhibitor prevented the reduction of surface GABA(A)Rs in injured hippocampal neurons subjected to oxygen-glucose deprivation, an in vitro insult that mimics ischemic injury, whereas a GABA(A)R antagonist significantly reduced this PTEN inhibitor-induced neuroprotection in both the in vitro and in vivo ischemic stroke models. CONCLUSIONS: Our study provides direct evidence that downregulation of PTEN protects against ischemic neuron death by preserving GABA(A)R function. Targeting this pathway may be an effective strategy for development of selective, potent stroke treatments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.000 | 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".