Mechanisms of vascular damage by systemic dissemination of the oral pathogen <i>Porphyromonas gingivalis</i>
Bibliographic record
Abstract
Several studies have shown a clear association between periodontal disease and increased risk of cardiovascular disease. Porphyromonas gingivalis (Pg), a key oral pathogen, and its cell surface-expressed gingipains, induce oedema in a zebrafish larvae infection model although the mechanism of these vascular effects is unknown. Here, we aimed to determine whether Pg-induced vascular damage is mediated by gingipains. In vitro, human endothelial cells from different vascular beds were invaded by wild-type (W83) but not gingipain-deficient (ΔK/R-ab) Pg. W83 infection resulted in increased endothelial permeability as well as decreased cell surface abundance of endothelial adhesion molecules PECAM-1 and VE-cadherin compared to infection with ΔK/R-ab. In agreement, when transgenic zebrafish larvae expressing fluorescently labelled PECAM-1 or VE-cadherin were systemically infected with W83 or ΔK/R-ab, a significant reduction in adhesion molecule fluorescence was observed specifically in endothelium proximal to W83 bacteria through a gingipain-dependent mechanism. Furthermore, this was associated with increased vascular permeability in vivo when assessed by dextran leakage microangiography. These data are the first to show that Pg directly mediates vascular damage in vivo by degrading PECAM-1 and VE-cadherin. Our data provide a molecular mechanism by which Pg might contribute to cardiovascular disease.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".