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Record W2777485706 · doi:10.1016/j.celrep.2017.12.008

Single-Molecule Quantification of Translation-Dependent Association of mRNAs with the Endoplasmic Reticulum

2017· article· en· W2777485706 on OpenAlexafffund
Franka Voigt, Hui Zhang, Xianying A. Cui, Désirée Triebold, Ai Xin Liu, Jan Eglinger, Eliza S. Lee, Jeffrey A. Chao, Alexander F. Palazzo

Bibliographic record

VenueCell Reports · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA and protein synthesis mechanisms
Canadian institutionsCanada Research ChairsUniversity of Toronto
FundersSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNatural Sciences and Engineering Research Council of CanadaNovartis Foundation
KeywordsEndoplasmic reticulumTranslation (biology)Cell biologyAssociation (psychology)ChemistryBiologyMessenger RNAComputational biologyBiochemistryGenePsychology

Abstract

fetched live from OpenAlex

It is well established that mRNAs encoding secretory or membrane-bound proteins are translated on the surface of the endoplasmic reticulum (ER). The extent to which mRNAs that encode cytosolic proteins associate with the ER, however, remains controversial. To address this question, we quantified the number of cytosolic protein-encoding mRNAs that co-localize with the ER using single-molecule RNA imaging in fixed and living cells. We found that a small but significant number of mRNAs that encode cytosolic proteins associate with the ER and show that this interaction is translation dependent. Furthermore, we demonstrate that cytosolic protein-encoding transcripts can remain on the ER with dwell times consistent with multiple rounds of translation and have higher ribosome occupancies than transcripts translated in the cytosol. These results advance our understanding of the diversity and dynamics of localized translation on the ER.

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 score0.013
Threshold uncertainty score0.216

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 head0.014
GPT teacher head0.227
Teacher spread0.212 · 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

Citations110
Published2017
Admission routes2
Has abstractyes

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