Faculty Opinions recommendation of Mono-uridylation of pre-microRNA as a key step in the biogenesis of group II let-7 microRNAs.
Bibliographic record
Abstract
RNase III Drosha initiates microRNA (miRNA) maturation by cleaving a primary miRNA transcript and releasing a pre-miRNA with a 2 nt 3' overhang. Dicer recognizes the 2 nt 3' overhang structure to selectively process pre-miRNAs. Here, we find that, unlike prototypic pre-miRNAs (group I), group II pre-miRNAs acquire a shorter (1 nt) 3' overhang from Drosha processing and therefore require a 3'-end mono-uridylation for Dicer processing. The majority of let-7 and miR-105 belong to group II. We identify TUT7/ZCCHC6, TUT4/ZCCHC11, and TUT2/PAPD4/GLD2 as the terminal uridylyl transferases responsible for pre-miRNA mono-uridylation. The TUTs act specifically on dsRNAs with a 1 nt 3' overhang, thereby creating a 2 nt 3' overhang. Depletion of TUTs reduces let-7 levels and disrupts let-7 function. Although the let-7 suppressor, Lin28, induces inhibitory oligo-uridylation in embryonic stem cells, mono-uridylation occurs in somatic cells lacking Lin28 to promote let-7 biogenesis. Our study reveals functional duality of uridylation and introduces TUT7/4/2 as components of the miRNA biogenesis pathway.Copyright © 2012 Elsevier Inc. All rights reserved. PMID: 23063654
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.219 | 0.107 |
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".