Transcriptomes in Healthy and Diseased Gingival Tissues
Bibliographic record
Abstract
BACKGROUND: Clinical and radiographic measures are gold standards for diagnosing periodontitis but offer little information regarding the pathogenesis of the disease. We hypothesized that a comparison of gene expression signatures between healthy and diseased gingival tissues would provide novel insights in the pathobiology of periodontitis and would inform the design of future studies. METHODS: Ninety systemically healthy non-smokers with moderate to advanced periodontitis (63 with chronic periodontitis and 27 with aggressive periodontitis) each contributed at least two diseased interproximal papillae (with bleeding on probing [BOP], probing depth [PD] > or =4 mm, and attachment loss [AL] > or =3 mm) and a healthy papilla, if available (no BOP, PD < or =4 mm, and AL < or =2 mm). RNA was extracted, amplified, reverse-transcribed, labeled, and hybridized with whole genome microarrays. Differential expression was assayed in 247 individual tissue samples (183 from diseased sites and 64 from healthy sites) using a standard mixed-effects linear model approach, with patient effects considered random with a normal distribution and gingival tissue status considered a two-level fixed effect. Gene ontology analysis classified the expression patterns into biologically relevant categories. RESULTS: Transcriptome analysis revealed that 12,744 probe sets were differentially expressed after adjusting for multiple comparisons (P <9.15 x 10(7)). Of those, 5,295 were upregulated and 7,449 were downregulated in disease compared to health. Gene ontology analysis identified 61 differentially expressed groups (adjusted P <0.05), including apoptosis, antimicrobial humoral response, antigen presentation, regulation of metabolic processes, signal transduction, and angiogenesis. CONCLUSION: Gingival tissue transcriptomes provide a valuable scientific tool for further hypothesis-driven studies of the pathobiology of periodontitis.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".