Initiation-mediated mRNA decay in yeast affects heat-shock mRNAs, and works through decapping and 5'-to-3' hydrolysis
Bibliographic record
Abstract
The degradation of mRNA in the yeast Saccharomyces cerevisiae takes place through several related pathways. In the most general mRNA-decay pathway, that of poly(A)-dependent decay, the normal shortening of the poly(A) tail on an mRNA molecule by deadenylation triggers mRNA decapping by the enzyme Dcp1p, followed by exonucleolytic digestion by Xrn1p. A specialized mRNA-decay pathway, termed nonsense-mediated decay, comes into play for mRNAs that contain an early nonsense codon. This pathway operates through the Upf proteins in addition to Dcp1p and Xrn1p. Previously, we identified a different specialized mRNA-decay pathway, the initiation-mediated decay pathway, and showed that it affects two Hsp70 heat-shock mRNAs under conditions of slowed translation initiation. Here we report that initiation-mediated mRNA decay also works through the Dcp1 and Xrn1 enzymes, and requires ongoing transcription by RNA polymerase II. We show that several other heat-shock mRNAs, including two from the Hsp90 gene family and three more from the Hsp70 gene family, are also subject to initiation-mediated decay, whereas a variety of non-heat-shock mRNAs are not affected.
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 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".