Gabapentin increases expression of δ subunit-containing GABAA receptors
Bibliographic record
Abstract
Background Gabapentin 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 (GABA A ) receptor function, nor does it modify synaptic inhibition. Here, we postulated that gabapentin increases expression of δ subunit-containing GABA A (δGABA A ) receptors that generate a tonic inhibitory conductance in multiple brain regions including the cerebellum and hippocampus. Methods Cell-surface biotinylation, Western blotting, electrophysiologic recordings, behavioral assays, high-performance liquid chromatography and gas chromatography-mass spectrometry studies were performed using mouse models. Findings Gabapentin enhanced expression of δGABA A 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 GABA A receptor agonists and neurosteroids in the brain were not altered by gabapentin. Interpretation These results provide compelling evidence to account for the GABAergic properties of gabapentin. Since reduced expression of δGABA A receptor occurs in several disorders, gabapentin may have much broader therapeutic applications than is currently recognized. Fund Supported 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 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) | 0.000 | 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) | 0.002 | 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 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".