Lipopolysaccharide induces a stromal–epithelial signalling axis in a rat model of chronic periodontitis
Bibliographic record
Abstract
AIM: Lipopolysaccharide is a bacterial virulence factor implicated in chronic periodontitis, which may penetrate the junctional epithelial barrier and basement membrane to insult underlying stroma. We sought to identify lipopolysaccharide-induced global gene expression changes responsible for signalling between stroma and epithelium during disease onset. MATERIALS AND METHODS: Using a rat lipopolysaccharide periodontitis model, junctional epithelium and underlying stromal tissue were separately collected from healthy and diseased animals by laser-capture microdissection and subject to gene expression microarray analysis. Key gene products identified were validated in gingival epithelial and fibroblast cell cultures. RESULTS: Global gene expression patterns distinguishing health versus disease were found in and between both tissue types. In stroma, the most significantly altered gene ontology function group (Z ≥ 4.00) was cytokines, containing most significantly (±2-fold; p < 0.05) upregulated genes amphiregulin, IL1-β and Fas ligand, all positive, diffusible modulators of the epithelial growth factor receptor pathway. In epithelium, the most significant changes were in downregulated FOS-related antigen-1 gene, somatostatin receptor-2 gene and mucin-4 gene, all negative modulators of the epithelial growth factor receptor pathway. CONCLUSION: These results establish a periodontitis model for studying gene product interactions and suggests that the onset of junctional epithelial disease hyperproliferation involves a concerted stromal-epithelial signalling axis.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".