Interaction of epidermal growth factor, Ca<sup>2+</sup>, and matrix metalloproteinase‐9 in primary keratinocyte migration
Bibliographic record
Abstract
Elevations of epidermal growth factor (EGF) and Ca(2+) concentrations in the wound site are associated with reepithelialization during wound healing. In addition, Ca(2+) and EGF can both induce increases in matrix metalloproteinase-9 (MMP-9) synthesis. However, little is known about the interplay of these events in regulating the migration properties of primary keratinocytes on collagen I, the most abundant extracellular matrix component in the skin. We found that EGF stimulated both chemokinetic and chemotactic migration of primary keratinocytes on collagen I; however, MMP-9 was required for EGF-stimulated chemotaxis but not EGF-stimulated chemokinesis. Calcium at 0.5 mM stimulated chemokinetic migration of keratinocytes. Together, Ca(2+) and EGF stimulated higher levels of chemokinesis than either stimulus alone. Furthermore, Ca(2+) could restore the ability of keratinocytes from MMP-9 null mice to undergo EGF-stimulated chemotaxis. The phosphatidylinositol-3 kinase inhibitor LY294002 inhibited both EGF- and Ca(2+)-stimulated chemokinetic migration. In contrast, the MEK inhibitor PD98059 blocked Ca(2+)- but not EGF-stimulated chemokinetic migration of keratinocytes. A combination of PD98059 and LY294002 was required to inhibit Ca(2+) enhancement of EGF-stimulated migration completely. Calcium-stimulated chemokinesis was completely blocked by either the protein kinase C-alpha inhibitor Gö6976 or the src/fyn inhibitor PP2. Using primary keratinocytes, our results showed how the combined action of Ca(2+), EGF, and MMP-9 regulated the contributions of extracellular-regulated kinase and phosphatidylinositol-3 kinase toward chemokinetic and chemotactic migration of keratinocytes.
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 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".