CD36/SR-B2-TLR2 Dependent Pathways Enhance Porphyromonas gingivalis Mediated Atherosclerosis in the Ldlr KO Mouse Model
Bibliographic record
Abstract
There is strong epidemiological association between periodontal disease and cardiovascular disease but underlying mechanisms remain ill-defined. Because the human periodontal disease pathogen, Porphyromonas gingivalis (Pg), interacts with innate immune receptors Toll-like Receptor (TLR) 2 and CD36/scavenger receptor-B2 (SR-B2), we studied how CD36/SR-B2 and TLR pathways promote Pg-mediated atherosclerosis. Western diet fed low density lipoprotein receptor knockout (Ldlr°) mice infected orally with Pg had a significant increase in lesion burden compared with uninfected controls.This increase was entirely CD36/SR-B2-dependent, as there was no significant change in lesion burden between infected and uninfected Cd36o/Ldlro mice [corrected]. Western diet feeding promoted enhanced CD36/SR-B2-dependent IL1β generation and foam cell formation as a result of Pg lipopolysaccharide (PgLPS) exposure. CD36/SR-B2 and TLR2 were necessary for inflammasome activation and optimal IL1ß generation, but also resulted in LPS induced lethality (pyroptosis). Modified forms of LDL inhibited Pg-mediated IL1ß generation in a CD36/SR-B2-dependent manner and prevented pyroptosis, but promoted foam cell formation. Our data show that Pg infection in the oral cavity can lead to significant TLR2-CD36/SR-B2 dependent IL1ß release. In the vessel wall, macrophages encountering systemic release of IL1ß, PgLPS and modified LDL have increased lipid uptake, foam cell formation, and release of IL1ß, but because pyroptosis is inhibited, this enables macrophage survival and promotes increased plaque development. These studies may explain increased lesion burden as a result of periodontal disease, and suggest strategies for development of therapeutics.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".