Epithelial Microvesicles Promote an Inflammatory Phenotype in Fibroblasts
Bibliographic record
Abstract
Microvesicles (MVs) are extracellular vesicles secreted by various cell types that are involved in intercellular communication. We hypothesized that in human periodontal disease, the pocket epithelium releases MVs, which then modulate gene expression in the underlying fibroblasts to control periodontal inflammation. MVs were isolated from culture medium of gingival epithelial cells (GECs) treated with oral bacterial biofilm extract or left untreated. Biofilm treatment significantly increased MV release from the GECs. Mass spectrometry of GEC-MVs identified a total of 2,173 proteins, of which about 80% were detected in MVs from both control and biofilm-treated GECs. Among 80 signature genes of human gingival fibroblasts, 20 were significantly regulated (P < 0.05) by MVs from control and biofilm-treated GECs in a similar manner. Matrix metalloproteinase 1 and 3 and interleukin 6 and 8 showed the strongest regulation at the mRNA and protein levels. Several cellular signaling pathways were activated by GEC-MVs in human gingival fibroblasts, including Smad and mitogen-activated protein kinase-associated pathways ERK1/2, JNK, and p38. However, ERK1/2 signaling dominated in the MV-induced gene expression changes. The results demonstrate that GEC-MVs have a strong regulatory effect on the expression of fibroblast genes associated with inflammation and matrix degradation and that bacterial biofilm stimulates the generation of GEC-MVs. This suggests that bacterial biofilms can contribute to the initiation and progression of periodontal disease by promoting a tissue-destructive phenotype in gingival fibroblasts via the enhanced secretion of epithelial MVs.
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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 | 0.001 | 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".