Subgingival bacterial colonization profiles correlate with gingival tissue gene expression
Bibliographic record
Abstract
BACKGROUND: Periodontitis is a chronic inflammatory disease caused by the microbiota of the periodontal pocket. We investigated the association between subgingival bacterial profiles and gene expression patterns in gingival tissues of patients with periodontitis. A total of 120 patients undergoing periodontal surgery contributed with a minimum of two interproximal gingival papillae (range 2-4) from a maxillary posterior region. Prior to tissue harvesting, subgingival plaque samples were collected from the mesial and distal aspects of each tissue sample. Gingival tissue RNA was extracted, reverse-transcribed, labeled, and hybridized with whole-genome microarrays (310 in total). Plaque samples were analyzed using checkerboard DNA-DNA hybridizations with respect to 11 bacterial species. Random effects linear regression models considered bacterial levels as exposure and expression profiles as outcome variables. Gene Ontology analyses summarized the expression patterns into biologically relevant categories. RESULTS: Wide inter-species variation was noted in the number of differentially expressed gingival tissue genes according to subgingival bacterial levels: Using a Bonferroni correction (p < 9.15 x 10(-7)), 9,392 probe sets were differentially associated with levels of Tannerella forsythia, 8,537 with Porphyromonas gingivalis, 6,460 with Aggregatibacter actinomycetemcomitans, 506 with Eikenella corrodens and only 8 with Actinomyces naeslundii. Cluster analysis identified commonalities and differences among tissue gene expression patterns differentially regulated according to bacterial levels. CONCLUSION: Our findings suggest that the microbial content of the periodontal pocket is a determinant of gene expression in the gingival tissues and provide new insights into the differential ability of periodontal species to elicit a local host response.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".