Pharmacological enhancement of δ-subunit-containing GABAA receptors that generate a tonic inhibitory conductance in spinal neurons attenuates acute nociception in mice
Bibliographic record
Abstract
The development of new strategies for the treatment of acute pain requires the identification of novel nonopioid receptor targets. This study explored whether δ-subunit-containing GABA(A)Rs (δGABA(A)Rs) in neurons of the spinal cord dorsal horn generate a tonic inhibitory conductance in vitro and whether δGABA(A)R activity regulates acute nociception. Whole-cell recordings revealed that δGABA(A)Rs generate a tonic inhibitory conductance in cultured spinal neurons and lamina II neurons in spinal cord slices. Increasing δGABA(A)R function by applying the δGABA(A)R-preferring agonist 4,5,6,7-tetrahydroisoxazolo [5,4-c]pyridine-3-ol (THIP) increased the tonic current and inhibited neuronal excitability in spinal neurons from wild-type (WT) but not δ subunit null-mutant (Gabrd(-/-)) mice. In behavioral studies, baseline δGABA(A)R activity did not regulate acute nociception; however, THIP administered intraperitoneally or intrathecally attenuated acute nociception in WT but not Gabrd(-/-) mice. In the formalin nociception assay, the phase 1 response was similar for WT and Gabrd(-/-) mice. In contrast, the phase 2 response, which models central sensitization, was greater in Gabrd(-/-) mice than WT. THIP administered intraperitoneally or intrathecally inhibited phase 1 responses of WT but not Gabrd(-/-) mice and had no effect on phase 2 responses of WT mice. Surprisingly, THIP reduced the enhanced phase 2 response in Gabrd(-/-) mice. Together, these results suggest that δGABA(A)Rs in spinal neurons play a major physiological and pharmacological role in the regulation of acute nociception and central sensitization. Spinal δ-subunit-containing GABA(A) receptors were identified with electrophysiological methods and behavioral models as novel targets for the treatment of acute pain.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".