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Record W3134679540 · doi:10.1242/jcs.255307

Development of conformational BRET biosensors that monitor ezrin, radixin and moesin activation in real time

2021· article· en· W3134679540 on OpenAlexafffund
Kévin Leguay, Barbara Decelle, Yu Yan He, Anthony Pagniez, Mireille Hogue, Hiroyuki Kobayashi, Christian Le Gouill, Michel Bouvier, Sébastien Carreno

Bibliographic record

VenueJournal of Cell Science · 2021
Typearticle
Languageen
FieldMedicine
TopicNeurofibromatosis and Schwannoma Cases
Canadian institutionsUniversité de MontréalInstitute for Research in Immunology and Cancer
FundersCanadian Cancer Society Research InstituteCanadian Institutes of Health Research
KeywordsMoesinEzrinRadixinBiologyCell biologyBiochemistryCytoskeletonCell

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.196
Threshold uncertainty score0.153

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.024
GPT teacher head0.268
Teacher spread0.244 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations13
Published2021
Admission routes2
Has abstractyes

Explore more

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