Protein‐protein interactions monitored in cells from transgenic mice using bioluminescence resonance energy transfer
Bibliographic record
Abstract
Monitoring the dynamics of protein-protein interactions in their natural environment remains a challenge. Resonance energy transfer approaches represent a promising avenue to directly probe these interactions in real time. The present study aims at establishing a proof of principle that bioluminescence resonance energy transfer (BRET) can be used to study the regulation of protein-protein interaction in cells from transgenic animals. A transgenic mouse line coexpressing the beta(2)-adrenergic receptor fused to Renilla luciferase (beta(2)AR-Rluc) and beta arrestin-2 fused to a green fluorescent protein (GFP2-beta arr2) was generated. The fusion proteins were found to be functional in the transgenic animals and the beta(2)AR-Rluc maintained pharmacological properties, comparable to that of the native receptor. Sufficiently high luminescence signal was generated to allow detection of BRET in testis cells where the beta(2)AR-Rluc transgene was expressed at levels significantly higher than that of the endogenous receptor in this tissue but remain within physiological range when compared with other beta(2)AR-expressing tissues. Stimulation with a beta-adrenergic agonist led to a significant dose- and time-dependent increase in BRET, which reflected ligand-promoted recruitment of beta arr2 to the receptor. Our study demonstrates that BRET can be used to monitor the dynamic regulation of protein-protein interactions in cells derived from transgenic mice.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".