E2F-1 Is Essential for Normal Epidermal Wound Repair
Bibliographic record
Abstract
E2F factors are involved in proliferation and apoptosis. To understand the role of E2F-1 in the epidermis, we screened wild type and E2F-1(-/-) keratinocyte mRNA for genes differentially expressed in the two cell populations. We demonstrate the reduced expression of integrins alpha(5), alpha(6), beta(1), and beta(4) in E2F-1(-/-) keratinocytes associated with reduced activation of Jun terminal kinase and Erk upon integrin stimulation. As a consequence of altered integrin expression and function, E2F-1(-/-) keratinocytes also show impaired migration, adhesion to extracellular matrix proteins, and a blunted chemotactic response to transforming growth factor-gamma1. E2F-1(-/-) keratinocytes, but not dermal fibroblasts, exhibit altered patterns of proliferation, including significant delays in transit through both G(1) and S phases of the cell cycle. Recognizing that proliferation and migration are key for proper wound healing in vivo, we postulated that E2F-1(-/-) mice may exhibit abnormal epidermal repair upon injury. Consistent with our hypothesis, E2F-1(-/-) mice exhibited impaired cutaneous wound healing. This defect is associated with substantially reduced local inflammatory responses and rates of re-epithelialization. Thus, we demonstrate that E2F-1 is indispensable for a hitherto unidentified cell type-specific and unique role in keratinocyte proliferation, adhesion, and migration as well as in proper wound repair and epidermal regeneration in vivo.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".