Targeting Morphine-Responsive Neurons: Generation of a Knock-In Mouse Line Expressing Cre Recombinase from the Mu-Opioid Receptor Gene Locus
Bibliographic record
Abstract
Abstract The mu-opioid receptor (MOR) modulates nociceptive pathways and reward processing, and mediates the strong analgesic and addictive properties of both medicinal as well as abused opioid drugs. MOR function has been extensively studied, and tools to manipulate or visualize the receptor protein are available. However, circuit mechanisms underlying MOR-mediated effects are less known, because genetic access to MOR-expressing neurons is lacking. Here we report the generation of a knock-inOprm1-Cre mouse line, which allows targeting and manipulating MOR opioid-responsive neurons. A cDNA encoding a T2A cleavable peptide and Cre recombinase fused to enhanced green fluorescent protein (EGFP/Cre) was inserted downstream of theOprm1gene sequence. The resultingOprm1-Cre line shows intactOprm1gene transcription. MOR and EGFP/Cre proteins are coexpressed in the same neurons, and localized in cytoplasmic and nuclear compartments, respectively. MOR signaling is unaltered, demonstrated by maintained DAMGO-induced G-protein activation, andin vivoMOR function is preserved as indicated by normal morphine-induced analgesia, hyperlocomotion, and sensitization. The Cre recombinase efficiently drives the expression of Cre-dependent reporter genes, shown by local virally mediated expression in the medial habenula and brain-wide fluorescence on breeding with tdTomato reporter mice, the latter showing a distribution patterns typical of MOR expression. Finally, we demonstrate that optogenetic activation of MOR neurons in the ventral tegmental area ofOprm1-Cre mice evokes strong avoidance behavior, as anticipated from the literature. TheOprm1-Cre line is therefore an excellent tool for both mapping and functional studies of MOR-positive neurons, and will be of broad interest for opioid, pain, and addiction research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".