Mu‐opioid receptors in tachykinin‐1‐positive cells mediate the respiratory and antinociceptive effects of the opioid fentanyl
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Opioid drugs are potent analgesics that carry the risk of respiratory side effects due to actions on μ-opioid receptors (MORs) in brainstem regions that control respiration. Substance P is encoded by the Tac1 gene and is expressed in neurons regulating breathing, nociception, and locomotion. Tac1-positive cells also express MORs in brainstem regions mediating opioid-induced respiratory depression. We determined the role of Tac1-positive cells in mediating the respiratory effects of opioid drugs. EXPERIMENTAL APPROACH: In situ hybridization was used to determine Oprm1 mRNA expression (gene encoding MORs) in Tac1-positive cells in regions regulating respiratory depression by opioid drugs. Conditional knockout mice lacking functional MORs in Tac1-positive cells were produced and the respiratory and locomotor responses to the opioid analgesic fentanyl were assessed using whole-body plethysmography. A tail immersion assay was used to assess the antinociceptive response to fentanyl. KEY RESULTS: Oprm1 mRNA was highly expressed (>80%) in subpopulations of Tac1-positive cells in the preBötzinger Complex, nucleus tractus solitarius, and Kölliker-Fuse/lateral parabrachial region. Conditionally knocking out MORs in Tac1-positive cells abolished the effects of fentanyl on respiratory rate, relative tidal volume, and relative minute ventilation compared with control mice. Importantly, the antinociceptive response of fentanyl was eliminated in mice lacking functional MORs in Tac1-positive cells, whereas locomotor effects induced by fentanyl were preserved. CONCLUSIONS AND IMPLICATIONS: Our findings suggest that Tac1-positive cells mediate the respiratory depressive and antinociceptive effects of the opioid fentanyl, providing important insights for the development of pain therapies with reduced risk of respiratory side 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) | 0.000 | 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".