Knock-In Mouse Models to Investigate the Functions of Opioid Receptors in vivo
Bibliographic record
Abstract
Due to their low expression levels, complex multi-pass transmembrane structure, and the current lack of highly specific antibodies, the assessment of endogenous G protein-coupled receptors (GPCRs) remains challenging. While most of the research regarding their functions was performed in heterologous systems overexpressing the receptor, recent advances in genetic engineering methods have allowed the generation of several unique mouse models. These animals proved to be useful to investigate numerous aspects underlying the physiological functions of GPCRs, including their endogenous expression, distribution, interactome, and trafficking processes. Given their significant pharmacological importance and central roles in the nervous system, opioid peptide receptors (OPr) are often referred to as prototypical receptors for the study of GPCR regulatory mechanisms. Although only a few GPCR knock-in mouse lines have thus far been generated, OPr are strikingly well represented with over 20 different knock-in models, more than half of which were developed within the last 5 years. In this review, we describe the arsenal of OPr (mu-, delta-, and kappa-opioid), as well as the opioid-related nociceptin/orphanin FQ (NOP) receptor knock-in mouse models that have been generated over the past years. We further highlight the invaluable contribution of such models to our understanding of the in vivo mechanisms underlying the regulation of OPr, which could be conceivably transposed to any other GPCR, as well as the limitations, future perspectives, and possibilities enabled by such tools.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".