The Hippo Pathway Effectors TAZ/YAP Regulate Dicer Expression and MicroRNA Biogenesis through Let-7
Bibliographic record
Abstract
MicroRNAs (miRNAs) are genome-encoded small double-stranded RNAs that have emerged as key regulators of gene expression and are implicated in most aspects of human development and disease. Canonical miRNA biogenesis involves processing of ∼70-nucleotide pre-miRNA hairpins by Dicer to generate mature ∼22-nucleotide miRNAs, which target complementary RNA sequences. Despite the importance of miRNA biogenesis, signaling mechanisms controlling this process are poorly defined. Here we demonstrate that the post-transcriptional regulation of Dicer is controlled by the cell density-mediated localization of the Hippo pathway effectors TAZ (transcriptional co-activator with PDZ-binding motif) and YAP (Yes-associated protein) (TAZ/YAP). We show that nuclear TAZ/YAP, which are abundant at low cell density, are required for efficient pre-miRNA processing. Knockdown of TAZ/YAP in low density cells, or density-mediated sequestration of TAZ/YAP into the cytoplasm, results in the defective processing of pre-miRNAs. Strikingly, one exception is Let-7, which accumulates upon loss of nuclear TAZ/YAP, leading to Let-7-dependent reduction in Dicer levels. Accordingly, inhibition of Let-7 rescues the miRNA biogenesis defects observed following TAZ/YAP knockdown. Thus, density-regulated TAZ/YAP localization defines a critical and previously unrecognized mechanism by which cells relay cell contact-induced cues to control miRNA biogenesis.
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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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".