Opposing roles for Egalitarian and Staufen in transport, anchoring and localization of oskar mRNA in the Drosophila oocyte
Bibliographic record
Abstract
Localization of oskar mRNA includes two distinct phases: transport from nurse cells to the oocyte, a process typically accompanied by cortical anchoring in the oocyte, followed by posterior localization within the oocyte. Signals within the oskar 3' UTR directing transport are individually weak, a feature previously hypothesized to facilitate exchange between the different localization machineries. We show that alteration of the SL2a stem-loop structure containing the oskar transport and anchoring signal (TAS) removes an inhibitory effect such that in vitro binding by the RNA transport factor, Egalitarian, is elevated as is in vivo transport from the nurse cells into the oocyte. Cortical anchoring within the oocyte is also enhanced, interfering with posterior localization. We also show that mutation of Staufen recognized structures (SRSs), predicted binding sites for Staufen, disrupts posterior localization of oskar mRNA just as in staufen mutants. Two SRSs in SL2a, one overlapping the Egalitarian binding site, are inferred to mediate Staufen-dependent inhibition of TAS anchoring activity, thereby promoting posterior localization. The other three SRSs in the oskar 3' UTR are also required for posterior localization, including two located distant from any known transport signal. Staufen, thus, plays multiple roles in localization of oskar mRNA.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".