A305 THE PRONOCICEPTIVE EFFECT OF HIGH DOSE OPIOIDS ON MOUSE DRG NEURONS IS MEDIATED BY DELTA OPIOID RECEPTORS
Bibliographic record
Abstract
Opioid drugs can be efficacious in the treatment of abdominal pain, but escalating doses of these drugs can also induce pronociceptive signalling in patients and paradoxically worsen pain. The analgesic actions of opioid drugs on dorsal root ganglia (DRG) neurons innervating the intestine are largely mediated by mu opioid receptors (MOR) but there is growing evidence that delta opioid receptors (DOR) could be important targets for pain signaling and to prevent opioid tolerance. However, it is unknown whether MOR and DOR are co-expressed on colonic DRG neurons and whether DOR play a role in pain signaling in the gut. The present study examined whether exposure to low and high concentrations of the opioid receptor agonist DAMGO had differing effects on the excitability of nociceptive DRG neurons, and if so, whether these actions involved MOR and/or DOR signaling. Single unit afferent recordings were obtained in situ from mouse colons to determine the effects of the MOR agonist DAMGO (100nM) and the DOR agonist DADLE (100nM) on mechanosensitive responses. To examine the effects of high and low dose opioids, nociceptive mouse DRG neurons were dissociated from control mice and incubated overnight with 10 nM or 10 µM DAMGO or media alone. Changes in neuronal excitability were recorded by measuring the rheobase using patch clamp recordings. To determine whether changes in cell excitability were mediated by MOR or DOR, neurons were incubated with the MOR antagonist CTOP (100 nM) or DOR antagonist SDM25N (1 µM) 30 min prior to the incubation with DAMGO. Single unit recordings from mouse colon were inhibited by both MOR and DOR agonists in 38% of afferents (3/8). Overnight incubation with 10 nM DAMGO inhibited neurons (rheobase increased by 43%, p <0.001) whereas 10 µM DAMGO had an excitatory effect (rheobase decreased by 31%, p <0.01). The inhibitory effect of low dose DAMGO was blocked by the MOR antagonist but the DOR antagonist had no effect. In contrast, the excitatory effect of the high dose DAMGO was completely blocked by the DOR antagonist. Many colonic DRG neurons co-express MOR and DOR. High concentrations of opioids paradoxically increase excitability of nociceptive DRG neurons, potentially by signaling to MOR-DOR heterodimers. This could lead to increased pain signaling and targeting DOR may be a potential target to mitigate this action. CCC
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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) | 0.005 | 0.001 |
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".