Human Gingival Fibroblasts Display a Non-Fibrotic Phenotype Distinct from Skin Fibroblasts in Three-Dimensional Cultures
Bibliographic record
Abstract
Scar formation following skin injury can be a major psychosocial and physiological problem. However, the mechanisms of scar formation are still not completely understood. Previous studies have shown that wound healing in oral mucosa is faster, associates with a reduced inflammatory response and results to significantly reduced scar formation compared with skin wounds. In the present study, we hypothesized that oral mucosal fibroblasts from human gingiva are inherently distinct from fibroblasts from breast and abdominal skin, two areas prone to excessive scar formation, which may contribute to the preferential wound healing outcome in gingiva. To this end, we compared the phenotype of human gingival and skin fibroblasts cultured in in vivo-like three-dimensional (3D) cultures that mimic the cells' natural extracellular matrix (ECM) niche. To establish 3D cultures, five parallel fibroblast lines from human gingiva (GFBLs) and breast skin (SFBLs) were seeded in high density, and cultured for up to 21 days in serum and ascorbic acid containing medium to induce expression of wound-healing transcriptome and ECM deposition. Cell proliferation, morphology, phenotype and expression of wound healing and scar related genes were analyzed by real-time RT-PCR, Western blotting and immunocytochemical methods. The expression of a set of genes was also studied in three parallel lines of human abdominal SFBLs. Findings showed that GFBLs displayed morphologically distinct organization of the 3D cultures and proliferated faster than SFBLs. GFBLs expressed elevated levels of molecules involved in regulation of inflammation and ECM remodeling (MMPs) while SFBLs showed significantly higher expression of TGF-β signaling, ECM and myofibroblast and cell contractility-related genes. Thus, GFBLs display an inherent phenotype conducive for fast resolution of inflammation and ECM remodeling, characteristic for scar-free wound healing, while SFBLs have a profibrotic, scar-prone phenotype.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".