RNA deadenylation complexes in development and diseases
Bibliographic record
Abstract
RNA deadenylation, the process of shortening of the 3' poly(A) tail of an RNA molecule, is one of the key steps of post-transcriptional regulation of gene expression in eukaryotic cells. PAN2/3 and CCR4-NOT (CNOT) are the two dominant RNA deadenylation complexes, which play central roles in mediating mRNA decay and translation. While degradation is the final fate of virtually all RNAs in their life cycles, selection of RNA targets as well as control of the rate and timing of RNA decay, in coordination with other molecular pathways, including translation, can be modulated in certain contexts. Such regulation influences cell growth, proliferation, and differentiation at the cellular level; and contributes to establish polarity and regulate signaling at the tissue level. Dysregulation of deadenylation processes have also been implicated in human diseases ranging from cardiac diseases and neurodevelopmental disorders to cancers. In this review, we will discuss mechanisms of gene expression control mediated by the RNA deadenylation complexes and highlight relevant evidence supporting the emerging roles of RNA deadenylation and its regulatory proteins during development and in diseases. A systemic understanding of these mechanisms will be a critical foundation for development of effective strategies to therapeutically target them.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".