Functional Impact of Collagens on the Activity Directed by the Promoter of the<i>α5</i>Integrin Subunit Gene in Corneal Epithelial Cells
Bibliographic record
Abstract
PURPOSE: The early step of corneal wound healing is characterized by the massive production of fibronectin (FN), whose secretion is progressively replaced by collagens from the basal membrane as wound healing proceeds. Here, we examined whether expression of the gene encoding the α5 subunit from the FN-binding integrin α5β1 changes as corneal epithelial cells (CECs) are cultured in the presence of collagen type I (CI) or type IV (CIV). METHODS: Responsiveness of the α5 gene toward collagen was determined by transfection of α5 promoter/chloramphenicol acetyltransferase (CAT) plasmids into rabbit and human CECs cultured on BSA or collagens. Electrophoretic mobility shift assays and Western blots were used to monitor the transcription factors required for basal α5 gene transcription in the presence of collagens. Gene profiling on microarrays was used to determine the impact of collagens on the patterns of genes expressed by CECs. RESULTS: All collagen types repressed the full-length α5/CAT promoter activity in confluent CECs. A moderate increase was observed in subconfluent rabbit CECs grown on CIV but not on CI. These collagen-dependent regulatory influences also correlated with alterations in the transcription factors Sp1/Sp3, NFI, and AP-1 that ensure α5 gene basal transcription. Microarray analyses revealed that CI more profoundly altered the pattern of genes expressed by human CECs than CIV. CONCLUSIONS: Collagens considerably suppressed α5 gene expression in CECs, suggesting that during wound healing, they may interfere with the influence FN exerts on CECs by altering their adhesive and migratory properties through a mechanism involving a reduction in α5 gene expression.
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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".