Development of conformational BRET biosensors that monitor ezrin, radixin and moesin activation in real time
Bibliographic record
Abstract
Ezrin, radixin and moesin compose the family of ERM proteins. They link actin filaments and microtubules to the plasma membrane to control signaling and cell morphogenesis. Importantly, their activity promotes invasive properties of metastatic cells from different cancer origins. Therefore, a precise understanding of how these proteins are regulated is important for the understanding of the mechanism controlling cell shape, as well as providing new opportunities for the development of innovative cancer therapies. Here, we developed and characterized novel bioluminescence resonance energy transfer (BRET)-based conformational biosensors, compatible with high-throughput screening, that monitor individual ezrin, radixin or moesin activation in living cells. We showed that these biosensors faithfully monitor ERM activation and can be used to quantify the impact of small molecules, mutation of regulatory amino acids or depletion of upstream regulators on their activity. The use of these biosensors allowed us to characterize the activation process of ERMs that involves a pool of closed-inactive ERMs stably associated with the plasma membrane. Upon stimulation, we discovered that this pool serves as a cortical reserve that is rapidly activated before the recruitment of cytoplasmic ERMs.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".