Connexin 43 Hemichannels Regulate the Expression of Wound Healing-Associated Genes in Human Gingival Fibroblasts
Bibliographic record
Abstract
Connexin 43 (Cx43) is the most ubiquitous connexin in various cells, and presents as hemichannels (HCs) and gap junctions (GJs) on the cell membrane. We have recently shown that Cx43 abundance was strongly reduced in fibroblasts of human gingival wounds, and blocking Cx43 function in cultured human gingival fibroblasts (GFBLs) strongly regulated the expression of wound healing-related genes. However, it is not known whether these responses involved Cx43 HCs or GJs. Here we show that Cx43 assembled into distinct GJ and HC plaques in GFBLs both in vivo and in vitro. Specific blockage of Cx43 HC function by TAT-Gap19, a Cx43 mimetic peptide, significantly upregulated the expression of several MMPs, TGF-β signaling molecules, Tenascin-C, and VEGF-A, while pro-fibrotic molecules, including several extracellular matrix proteins and myofibroblast and cell contractility-related molecules, were significantly downregulated. These changes were linked with TAT-Gap19-induced suppression of ATP signaling and activation of the ERK1/2 signaling pathway. Collectively, our data suggest that reduced Cx43 HC function could promote fast and scarless gingival wound healing. Thus, selective suppression of Cx43 HCs may provide a novel target to modulate wound healing.
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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".