βγ G-proteins, but not regulators of G-protein signaling 4, modulate opioid-induced respiratory rate depression
Bibliographic record
Abstract
Opioid medications are the mainstay of pain management but present substantial side-effects such as respiratory depression which can be lethal with overdose. Most opioid drugs, such as fentanyl, act on opioid receptors such as the G-protein-coupled µ-opioid receptors (MOR). G-protein-coupled receptors activate pertussis toxin-sensitive G-proteins to inhibit neuronal activity. Binding of opioid ligands to MOR and subsequent activation G proteins βγ is modulated by regulator of G-protein signaling (RGS). The roles of G-proteins βγ and RGS in MOR-mediated inhibition of the respiratory network are not known. Using rodent models to pharmacologically modulate G-protein signaling, we aim to determine the roles of βγ G-proteins and RGS4. We showed that inhibition of βγ G-proteins using gallein perfused in the brainstem circuits regulating respiratory depression by opioid drugs results in complete reversal of respiratory depression. Blocking of RGS4 using CCG55014 did not change the respiratory depression induced by MOR activation despite co-expression of RGS4 and MORs in the brainstem. Our results suggest that neuronal inhibition by opioid drugs is mediated by G-proteins, but not by RGS4, which supports the concept that βγ G-proteins could be molecular targets to develop opioid overdose antidotes without the risks of re-narcotization often found with highly potent opioid drugs. On the other hand, RGS4 mediates opioid analgesia, but not respiratory depression, and RGS4 may be molecular targets to develop pain therapies without respiratory liability.
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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 | 0.001 |
| 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".