Faculty Opinions recommendation of Competition for XPO5 binding between Dicer mRNA, pre-miRNA and viral RNA regulates human Dicer levels.
Bibliographic record
Abstract
MicroRNAs (miRNAs) are a class of small, noncoding RNAs that function by regulating gene expression post-transcriptionally.Alterations in miRNA expression can strongly influence cellular physiology.Here we demonstrated cross-regulation between two components of the RNA interference (RNAi) machinery in human cells.Inhibition of exportin-5, the karyopherin responsible for pre-miRNA export, downregulated expression of Dicer, the RNase III required for pre-miRNA maturation.This effect was post-transcriptional and resulted from an increased nuclear localization of Dicer mRNA.In vitro assays and cellular RNA immunoprecipitation experiments showed that exportin-5 interacted directly with Dicer mRNA.Titration of exportin-5 by overexpression of either pre-miRNA or the adenoviral VA1 RNA resulted in loss of Dicer mRNA-exportin-5 interaction and reduction of Dicer level.This saturation also occurred during adenoviral infection and enhanced viral replication.Our study reveals an important crossregulatory mechanism between pre-miRNA or viral small RNAs and Dicer through exportin-5.miRNAs are single-stranded RNA of 19-24 nucleotides that are predicted to regulate up to 30% of protein-encoding genes.miRNA have been implicated in a vast array of cellular processes including cell differentiation, proliferation and apoptosis 1 .miRNA repertoires are highly cell type specific and change markedly during development or upon cell activation 2 .Changes in miRNA expression profile have been linked to human pathologies such as cancer and neurodegenerative diseases 3 .In the nucleus, primary RNA polymerase II
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.457 | 0.180 |
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".