Fentanyl-Induced Respiratory Depression and Locomotor Hyperactivity Are Mediated by μ-Opioid Receptors Expressed in Somatostatin-Negative Neurons
Bibliographic record
Abstract
Abstract Opioid drugs are widely used as analgesics but cause respiratory depression, a potentially lethal side effect with overdose, by acting on μ-opioid receptors (MORs) expressed in brainstem regions involved in the control of breathing. Although many brainstem regions have been shown to regulate opioid-induced respiratory depression, the types of neurons involved have not been identified. Somatostatin is a major neuropeptide found in brainstem circuits regulating breathing, but it is unknown whether somatostatin-expressing circuits regulate respiratory depression by opioids. We examined the coexpression ofSst(gene encoding somatostatin) andOprm1(gene encoding MORs) mRNAs in brainstem regions involved in respiratory depression. Interestingly,Oprm1mRNA expression was found in the majority (>50%) ofSst-expressing cells in the preBötzinger Complex, the nucleus tractus solitarius, the nucleus ambiguus, and the Kölliker–Fuse nucleus. We then compared respiratory responses to fentanyl between wild-type andOprm1full knock-out mice and found that the lack of MORs prevented respiratory rate depression from occurring. Next, using transgenic knock-out mice lacking functional MORs specifically inSst-expressing cells, we compared respiratory responses to fentanyl between control and the conditional knock-out mice. We found that respiratory rate depression by fentanyl was preserved when MORs were deleted only inSst-expressing cells. Our results show that despite coexpression ofSstandOprm1in respiratory circuits and the importance of somatostatin-expressing cells in the regulation of breathing, these cells do not mediate opioid-induced respiratory rate depression. Instead, MORs found in respiratory cell populations other thanSst-expressing cells likely contribute to the respiratory effects of fentanyl.
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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) | 0.001 | 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".