Monitoring Signalling and Trafficking of Neurotensin Type 1 Receptor in Animal Model using Fluorescent‐based Methods
Bibliographic record
Abstract
Neurotensin Type 1 Receptor (NTSR1) is a G Protein‐Coupled Receptor (GPCR) with proposed roles in cancer, neuropsychiatric and neurodegenerative diseases, eating disorders, pain, as well as inflammation. It therefore represents a potential therapeutic target for the treatment of these diseases. However, relatively little is known about the repertoire of signalling effectors that can be engaged by NTSR1 and about the functional selectivity of drug candidates targeting this receptor in native tissue. In a first step toward generating an animal model allowing to monitor the activation and regulation of NTSR1 in native tissues, we developed a DNA construct expressing NTSR1 tagged to vYFP at its C‐terminus. We investigated the signalling responses of NTSR1‐vYFP promoted by eight agonists and two antagonists, and compared them to those obtained for the unmodified NTSR1. For this purpose, we used a suite of BRET‐based sensors expressed in HEK‐293 cells. We showed that NTSR1‐vYFP has the same signalling profile as the endogenous NTSR1 as it activates both β‐Arrestin 1 and 2 and all G proteins except G 12 and G i3 . Moreover, we confirmed that ML314 is a biased agonist toward β‐Arrestin pathways and we showed that PD149163 does not activate G s protein. Because the vYFP fusion does not seem to modify the signalling profile of the NTSR1, we have embarked on developing a knock‐in mouse model expressing NTSR1‐vYFP instead of the native receptor which will be a useful tool to study receptor signaling and trafficking under physiological conditions. Support or Funding Information The project is funded by the Consortium de recherche biopharmaceutique (CQDM). Pierre Couvineau is funded by the INSERM‐FRQS Postdoctoral Exchange Program This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".