The RNA-binding protein Quaking regulates multiciliated and basal cell abundance in the developing lung
Bibliographic record
Abstract
RNA-binding proteins (RBPs) form complexes with RNA, changing how the RNA is processed and thereby regulating gene expression. RBPs are important sources of gene regulation during organogenesis, including the development of lungs. The RBP called Quaking (QK) is critical for embryogenesis, yet it has not been studied in the developing lung. Here, we show that QK is widely expressed during rat lung development and into adulthood. The QK isoforms QK5 and QK7 colocalize to the nuclei of nearly all lung cells. QK6 is present in the nuclei and cytoplasm of mesenchymal cells and is only present in the epithelium during branching morphogenesis. QK knockdown in embryonic lung explants caused a greater number of multiciliated cells to appear in the airways, at the expense of basal cells. The mRNA of multiciliated cell genes and the abundance of FOXJ1/SOX2+ cells increased after knockdown, whereas P63/SOX2+ cells decreased. The cytokine IL-6, a known regulator of multiciliated cell differentiation, had increased mRNA levels after QK knockdown, although protein levels remained unchanged. Further studies are necessary to confirm whether QK acts as a blocker for the IL-6-induced differentiation of basal cells into multiciliated cells, and a conditional QK knockout would likely lead to additional discoveries on QK's role during lung development.
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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".