The EDC4‐XRN1 interaction controls P‐body dynamics to link mRNA decapping with decay
Bibliographic record
Abstract
Deadenylation‐dependent mRNA decapping and decay is the major cytoplasmic mRNA turnover pathway in eukaryotes. Many mRNA decapping and decay factors are associated with each other via protein–protein interaction motifs. For example, the decapping enzyme DCP2 and the 5'–3' exonuclease XRN1 interact with the enhancer of mRNA‐decapping protein 4 (EDC4), a large scaffold that has been reported to stimulate mRNA decapping. mRNA decapping and decay factors are also found in processing bodies (P‐bodies), evolutionarily conserved ribonucleoprotein granules that are often enriched with mRNAs targeted for decay, yet paradoxically are not required for mRNA decay to occur. Here, we show that disrupting the EDC4‐XRN1 interaction or altering their stoichiometry inhibits mRNA decapping, with microRNA‐targeted mRNAs being stabilized in a translationally repressed state. Importantly, we demonstrate that this concomitantly leads to larger P‐bodies that are responsible for preventing mRNA decapping. Finally, we demonstrate that P‐bodies support cell viability and prevent stress granule formation when XRN1 is limiting. Taken together, these data demonstrate that the interaction between XRN1 and EDC4 regulates P‐body dynamics to properly coordinate mRNA decapping with 5′–3′ decay in human cells. Although P‐bodies are highly concentrated in mRNA decay factors, P‐bodies are surprisingly not required for mRNA decay to occur. Here, P‐bodies are shown to inhibit mRNA decapping and to promote mRNA translational inhibition when the 5′–3′ decay pathway is disrupted. P‐bodies remodel when the 5′–3′ exonuclease XRN1 becomes limiting to inhibit the decapping of P‐body‐enriched mRNAs.
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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".