A local translation program regulates centriole amplification in the airway epithelium
Bibliographic record
Abstract
Biogenesis of organelles requires targeting of a subset of proteins to specific subcellular domains by signal peptides or mechanisms controlling mRNA localization and local translation. How local distribution and translation of specific mRNAs for organelle biogenesis is achieved remains elusive and likely to be dependent on the cellular context. Here we identify Trinucleotide repeat containing-6a (Tnrc6a), a component of the miRNA pathway, distinctively localized to apical granules of differentiating airway multiciliated cells (MCCs) adjacent to centrioles. In spite of being enriched in TNRC6A and the miRNA-binding protein AGO2, they lack enzymes for mRNA degradation. Instead, we found these apical granules enriched in components of the mRNA translation machinery and newly synthesized proteins suggesting that they are specific hubs for target mRNA localization and local translation in MCCs. Consistent with this, Tnrc6a loss of function prevented formation of these granules and led to a broad reduction, rather than stabilization of miRNA targets. These included downregulation of key genes involved in ciliogenesis and was associated with defective multicilia formation both in vivo and in primary airway epithelial cultures. Similar analysis of Tnrc6a disruption in yolk sac showed stabilization of miRNA targets, highlighting the potential diversity of these mechanisms across organs.
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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) | 0.001 | 0.000 |
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".