P-bodies directly regulate MARF1-mediated mRNA decay in human cells
Bibliographic record
Abstract
Processing bodies (P-bodies) are ribonucleoprotein granules that contain mRNAs, RNA-binding proteins and effectors of mRNA turnover. While P-bodies have been reported to contain translationally repressed mRNAs, a causative role for P-bodies in regulating mRNA decay has yet to be established. Enhancer of decapping protein 4 (EDC4) is a core P-body component that interacts with multiple mRNA decay factors, including the mRNA decapping (DCP2) and decay (XRN1) enzymes. EDC4 also associates with the RNA endonuclease MARF1, an interaction that antagonizes the decay of MARF1-targeted mRNAs. How EDC4 interacts with MARF1 and how it represses MARF1 activity is unclear. In this study, we show that human MARF1 and XRN1 interact with EDC4 using analogous conserved short linear motifs in a mutually exclusive manner. While the EDC4-MARF1 interaction is required for EDC4 to inhibit MARF1 activity, our data indicate that the interaction with EDC4 alone is not sufficient. Importantly, we show that P-body architecture plays a critical role in antagonizing MARF1-mediated mRNA decay. Taken together, our study suggests that P-bodies can directly regulate mRNA turnover by sequestering an mRNA decay enzyme and preventing it from interfacing with and degrading targeted 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) | 0.002 | 0.001 |
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".