α5-GABA-A Receptor Positive Allosteric Modulation prevents neuronal atrophy and cognitive decline independently of p-Tau accumulation in the PS19 mouse model
Bibliographic record
Abstract
ABSTRACT Background Dysregulated Tau phosphorylation (p-Tau) is a hallmark of neurodegenerative disorders such as Alzheimer’s disease (AD) or frontotemporal dementia (FTD), resulting in neurofibrillary tangle accumulation, neuronal atrophy and cognitive impairment. Impaired somatostatin (SST) expression and reduced SST-expressing GABAergic neurons significantly contributes to AD-related pathophysiology and correlates with cognitive impairment. SST+ interneurons inhibit the dendrites of excitatory neurons in cortical layers and hippocampus, primarily through α5-GABA-A receptors, regulating cognitive function. Leveraging a newly developed small molecule that targets the α5-GABA-A receptors via positive allosteric modulation (α5-PAM), we assessed its effects on p-Tau-related neuronal morphology, cognitive deficits and protein expression. Methods In the PS19 transgenic mouse model, we administered the α5-PAM, GL-II-73, either acutely or chronically at 3 and 6 months, corresponding to early and advanced stage of p-Tau accumulation. Golgi staining analyzed dendritic morphology and spine density in mice chronically exposed to α5-PAM. Western blotting was used to quantify p-tau and Tau expression. Spatial working memory was assessed using the Y-maze. Results Chronic treatment at 3 and 6 months mitigated p-Tau-induced loss of spine density and reduced dendritic length. α5-PAM treatment did not affect p-tau levels. α5-PAM effectively reversed spatial working memory deficits induced by p-tau accumulation both acutely and chronically. Conclusions α5-GABA-A receptor positive allosteric modulation displayed neurotrophic (spine and dendritic pathology) and procognitive (working memory) effects in the PS19 model, independently of p-Tau burden. This suggests a novel therapeutic strategy for p-Tau-related pathologies with both symptomatic and disease-modifying potential.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".