Transcription Factor p63 Regulates Key Genes and Wound Repair in Human Airway Epithelial Basal Cells
Bibliographic record
Abstract
The airway epithelium in asthma displays altered repair and incomplete barrier formation. Basal cells are the progenitor cells of the airway epithelium, and can repopulate other cell types after injury. We previously reported increased numbers of basal cells expressing the transcription factor p63 in the airway epithelium of patients with asthma. Here we sought to determine the molecular consequences of p63 expression in basal human airway epithelial cells during wound repair. Because at least six isoforms of p63 exist (N-terminally truncated [ΔN] versus transcriptional activation promoter variants and α, β, or γ 3' splice variants), the expression of all isoforms was investigated in primary human airway epithelial cells (pHAECs). We modulated p63 expression, using small interfering RNA (siRNA) and adenoviral constructs to determine the effects of p63 on 21 candidate target genes by RT-PCR, and on repair using a scratch wound assay. We found that basal pHAECs from asthmatic and nonasthmatic donors predominantly expressed the N-terminally truncated p63α variant (ΔNp63α) isoform, with no disease-specific differences in expression. The knockdown of ΔNp63, using specific siRNA, decreased the expression of 11 out of 21 genes associated with epithelial repair and differentiation, including β-catenin, epidermal growth factor receptor, and Jagged1. The loss of ΔNp63 significantly inhibited wound closure (which was associated with the decreased expression of β-catenin and Jagged1), reduced epithelial proliferation as measured by Ki-67 staining, and increased E-cadherin expression, potentially preventing cytokinesis. In conclusion, ΔNp63α is the major isoform expressed in basal pHAECs, and is essential for epithelial wound repair. The role of ΔNp63α in epithelial barrier integrity requires further study to understand its role in health and disease.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".