Gabapentin increases expression of δ subunit-containing GABAA receptors
Bibliographic record
Abstract
BackgroundGabapentin is a structural analog of the inhibitory neurotransmitter γ-aminobutyric acid (GABA). Its anticonvulsant, analgesic and anxiolytic properties suggest that it increases GABAergic inhibition; however, the molecular basis for these effects is unknown as gabapentin does not directly modify GABA type A (GABAA) receptor function, nor does it modify synaptic inhibition. Here, we postulated that gabapentin increases expression of δ subunit-containing GABAA (δGABAA) receptors that generate a tonic inhibitory conductance in multiple brain regions including the cerebellum and hippocampus.MethodsCell-surface biotinylation, Western blotting, electrophysiologic recordings, behavioral assays, high-performance liquid chromatography and gas chromatography-mass spectrometry studies were performed using mouse models.FindingsGabapentin enhanced expression of δGABAA receptors and increased a tonic inhibitory conductance in neurons. This increased expression likely contributes to GABAergic effects as gabapentin caused ataxia and anxiolysis in wild-type mice but not δ subunit null-mutant mice. In contrast, the antinociceptive properties of gabapentin were observed in both genotypes. Levels of GABAA receptor agonists and neurosteroids in the brain were not altered by gabapentin.InterpretationThese results provide compelling evidence to account for the GABAergic properties of gabapentin. Since reduced expression of δGABAA receptor occurs in several disorders, gabapentin may have much broader therapeutic applications than is currently recognized.FundSupported by a Foundation Grant (FDN-154312) from the Canadian Institutes of Health Research (to B.A.O.); a NSERC Discovery Grant (RGPIN-2016-05538), a Canada Research Chair in Sensory Plasticity and Reconsolidation, and funding from the University of Toronto Centre for the Study of Pain (to R.P.B.).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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".