Posttranscriptional regulation of maternal transcripts by the PAN GU kinase in the early Drosophila embryo
Bibliographic record
Abstract
Early embryonic development is controlled by maternally deposited proteins and transcripts. Egg activation triggers a cascade of posttranscriptional mechanisms that are crucial to the regulation of these maternal mRNAs during this time of transcriptional quiescence. These mechanisms include translational activation, repression and transcript destabilization. Here I show that, in Drosophila, the PAN GU (PNG) kinase complex sits near the top of this cascade that ultimately leads to the destabilization of maternal mRNAs. The genes png, plutonium (plu) and giant nuclei (gnu), which encode the components of this complex, were recovered in a screen for maternal effect lethal mutants which fail to undergo degradation. I show that png's control of transcript destabilization is genetically separable and therefore independent of its well characterized role in the cell cycle. PNG acts following egg activation in promoting the translation of SMAUG (SMG), a major posttranscriptional regulator. Our gene-expression profiling experiments show that SMG is responsible for targeting two thirds of degrading maternal mRNAs. PNG activates smg translation in a poly(A)-independent manner acting through the smg 3'UTR. Finally, I show that PNG also has a SMG-independent mechanism of eliciting transcript decay.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".