Positive Allosteric Modulation of α5-GABAA Receptors Reverses Stress-Induced Alterations in Dopamine System Function and Prepulse Inhibition of Startle
Bibliographic record
Abstract
BACKGROUND: Up to 64% of patients diagnosed with posttraumatic stress disorder (PTSD) experience psychosis, likely attributable to aberrant dopamine neuron activity. We have previously demonstrated that positive allosteric modulators of α5-GABAARs can selectively decrease hippocampal activity and reverse psychosis-like physiological and behavioral alterations in a rodent model used to study schizophrenia; however, whether this approach translates to a PTSD model remains to be elucidated. METHODS: We utilized a 2-day inescapable foot shock (IS) procedure to induce stress-related pathophysiology in male Sprague-Dawley rats. We evaluated the effects of intra-ventral hippocampus (vHipp) administration GL-II-73, an α5-GABAAR, or viral overexpression of the α5 subunit, using in vivo electrophysiology and behavioral measures in control and IS-treated rats. RESULTS: IS significantly increased ventral tegmental area dopamine neuron population activity, or the number of dopamine neurons firing spontaneously (n = 6; P = .016), consistent with observation in multiple rodent models used to study psychosis. IS also induced deficits in sensorimotor gating, as measured by reduced prepulse inhibition of startle (n = 12; P = .039). Interestingly, intra-vHipp administration of GL-II-73 completely reversed IS-induced increases in dopamine neuron population activity (n = 6; P = .024) and deficits in prepulse inhibition (n = 8; P = .025), whereas viral overexpression of the α5 subunit in the vHipp was not effective. CONCLUSIONS: Our results demonstrate that pharmacological intervention augmenting α5-GABAAR function, but not α5 overexpression in itself, can reverse stress-induced deficits related to PTSD in a rodent model, providing a potential site of therapeutic intervention to treat comorbid psychosis in PTSD.
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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 | 0.000 | 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".