A genetically encoded biosensor reveals location bias of opioid drug action
Bibliographic record
Abstract
Summary Opioid receptors (ORs) precisely modulate behavior when activated by native peptide ligands but distort behaviors to produce pathology when activated by non-peptide drugs. A fundamental question is how drugs differ from peptides in their actions on target neurons. One way that drugs can differ is by imposing selective effects on the conformational equilibrium of individual ORs. We wondered if drugs can also impose selective effects on the location of OR activation in individual OR-expressing neurons. Here we develop a genetically encoded biosensor that directly localizes ligand-induced activation and deactivation of ORs in living cells, and use it to generate the first real-time map of the spatiotemporal organization of μ‐ and δ-OR activation in neurons. Peptide agonists produce a characteristic activation pattern initiated in the plasma membrane and propagating to endosomes after receptor internalization. Drugs produce a different activation pattern by uniquely driving OR activation in the somatic Golgi apparatus and extending throughout the dendritic arbor in Golgi outposts. These results demonstrate a new approach to probe the cellular basis of neuromodulation and reveal that drugs profoundly distort the spatiotemporal landscape of neuronal OR activation.
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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".