Localization of <scp>mRNAs</scp> to the endoplasmic reticulum
Bibliographic record
Abstract
Almost all cells use mRNA localization to establish spatial control of protein synthesis. One of the best-studied examples is the targeting and anchoring of mRNAs encoding secreted, organellar, and membrane-bound proteins to the surface of the endoplasmic reticulum (ER). In this review, we provide a historical perspective on the research that elucidated the canonical protein-mediated targeting of nascent-chain ribosome mRNA complexes to the surface of the ER. We then discuss subsequent studies which provided concrete evidence that a subpopulation of mRNAs utilize a translation-independent mechanism to localize to the surface of this organelle. This alternative mechanism operates alongside the signal recognition particle (SRP) mediated co-translational targeting pathway to promote proper mRNA localization to the ER. Recent work has uncovered trans-acting factors, such as the mRNA receptor p180, and cis-acting elements, such as transmembrane domain coding regions, that are responsible for this alternative mRNA localization process. Furthermore, some unanticipated observations have raised the possibility that this alternative pathway may be conserved from bacteria to mammalian cells.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".