Symptomatic and Neurotrophic Effects of GABAA Receptor Positive Allosteric Modulation in a Mouse Model of Chronic Stress
Bibliographic record
Abstract
Abstract Chronic stress is a risk factor for Major depressive disorder (MDD), and in rodents, it recapitulates human behavioral, cellular and molecular changes. In MDD and after chronic stress, neuronal dysfunctions and deficits in GABAergic signaling are observed and responsible for symptom severity. GABA signals predominantly through GABAA receptors (GABAA-R) composed of various subunit types that relate to downstream outcomes. Activity at α2-GABAA-Rs contributes to anxiolytic properties, α5-GABAA-Rs to cognitive functions, and α1-GABAA-Rs to sedation. Therefore, a therapy aiming at increasing α2- and α5-GABAA-Rs activity, but devoid of α1-GABAA-R activity, has potential to address several symptomologies of depression while avoiding side effects. This study investigated the activity profiles and behavioral efficacy of two molecules enantiomers of each other (GL-II-73 and GL-I-54), separately and as a racemic mixture (GL-RM), and potential disease-modifying effects on neuronal morphology. Results confirm GL-I-54 and GL-II-73 exert positive allosteric modulation at the α2-, α3-, α5-GABAA-Rs and α5-containing GABAA-Rs, respectively, and have anti-depressant and pro-cognitive effects independently. Using unpredictable chronic mild stress (UCMS) in male and female mice (n=12/group), we show that acute and chronic administration of GL-RM combined the anti-depressant and pro-cognitive effects of each enantiomer, although at lower doses avoiding sedation. Morphology studies showed reversal of spine density loss caused by UCMS after chronic GL-RM treatment at apical and basal dendrites of the PFC and CA1. Together, these results support using a racemic mixture with combined α2-, α3-, α5-GABAA-R profile to reverse chronic stress-induced mood symptoms, cognitive deficits, and with anti-stress neurotrophic effects.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".