Eukaryotic Initiation Factor 4G Suppresses Nonsense-Mediated mRNA Decay by Two Genetically Separable Mechanisms
Bibliographic record
Abstract
Nonsense-mediated mRNA decay (NMD), which is best known for degrading mRNAs with premature termination codons (PTCs), is thought to be triggered by aberrant translation termination at stop codons located in an environment of the mRNP that is devoid of signals necessary for proper termination. In mammals, the cytoplasmic poly(A)-binding protein 1 (PABPC1) has been reported to promote correct termination and therewith antagonize NMD by interacting with the eukaryotic release factors 1 (eRF1) and 3 (eRF3). Using tethering assays in which proteins of interest are recruited as MS2 fusions to a NMD reporter transcript, we show that the three N-terminal RNA recognition motifs (RRMs) of PABPC1 are sufficient to antagonize NMD, while the eRF3-interacting C-terminal domain is dispensable. The RRM1-3 portion of PABPC1 interacts with eukaryotic initiation factor 4G (eIF4G) and tethering of eIF4G to the NMD reporter also suppresses NMD. We identified the interactions of the eIF4G N-terminus with PABPC1 and the eIF4G core domain with eIF3 as two genetically separable features that independently enable tethered eIF4G to inhibit NMD. Collectively, our results reveal a function of PABPC1, eIF4G and eIF3 in translation termination and NMD suppression, and they provide additional evidence for a tight coupling between translation termination and initiation.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".