Ackr3-Venus knock-in mouse lights up brain vasculature
Bibliographic record
Abstract
The atypical chemokine receptor 3, ACKR3, is a G protein-coupled receptor, which does not couple to G proteins but recruits βarrestins. At present, ACKR3 is considered a target for cancer and cardiovascular disorders, but less is known about the potential of ACKR3 as a target for brain disease. Further, mouse lines have been created to identify cells expressing the receptor, but there is no tool to visualize and study the receptor itself under physiological conditions. Here, we engineered a knock-in (KI) mouse expressing a functional ACKR3-Venus fusion protein to directly detect the receptor, particularly in the adult brain. In HEK-293 cells, native and fused receptors showed similar membrane expression, ligand induced trafficking and signaling profiles, indicating that the Venus fusion does not alter receptor signaling. We also found that ACKR3-Venus enables direct real-time monitoring of receptor trafficking using resonance energy transfer. In ACKR3-Venus knock-in mice, we found normal ACKR3 mRNA levels in the brain, suggesting intact gene transcription. We fully mapped receptor expression across 14 peripheral organs and 112 brain areas and found that ACKR3 is primarily localized to the vasculature in these tissues. In the periphery, receptor distribution aligns with previous reports. In the brain there is notable ACKR3 expression in endothelial vascular cells, hippocampal GABAergic interneurons and neuroblast neighboring cells. In conclusion, we have generated Ackr3-Venus knock-in mice with a traceable ACKR3 receptor, which will be a useful tool to the research community for interrogations about ACKR3 biology and related diseases.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.009 | 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".