Global <scp>TLR</scp>2 and 4 deficiency in mice impacts bone resorption, inflammatory markers and atherosclerosis to polymicrobial infection
Bibliographic record
Abstract
Summary Toll‐like‐receptors (TLRs) play a significant role in the generation of a specific innate immune response against invading pathogens. TLR2 and TLR4 signaling contributes to infection‐induced inflammation in periodontal disease (PD) and atherosclerosis. Observational studies point towards a relationship between PD and atherosclerosis, but the role of TLR2 and TLR4 in the recognition of multiple oral pathogens and their modulation of host response leading to atherosclerosis are not clear. We evaluated the role of TLR2 and TLR4 signaling in the induction of both PD and atherosclerosis in TLR2−/− and TLR4−/− mice to polymicrobial infection with periodontal pathogens Porphyromonas gingivalis, Treponema denticola, Tannerella forsythia, and Fusobacterium nucleatum. Polybacterial infections have established gingival colonization in TLR2−/− and TLR4−/− mice and induction of a pathogen‐specific immunoglobulin G immune response. But TLR deficiency dampened accelerated alveolar bone resorption and intrabony defects, indicating a central role in infection‐induced PD. Periodontal bacteria disseminated from gingival tissue to the heart and aorta through intravascular dissemination; however, there was no increase in atherosclerosis progression in the aortic arch. Polybacterial infection does not alter levels of serum risk factors such as oxidized low‐density lipoprotein, nitric oxide, and lipid fractions in both mice. Polymicrobial‐infected TLR2−/− mice demonstrated significant levels (P < 0.05 to P < 0.01) of T helper type 2 [transforming growth factor‐β1, macrophage inflammatory protein‐3α, interleukin‐13 (IL‐13)] and T helper type 17 (IL‐17, IL‐21, IL‐22, IL‐23) splenic T‐cell cytokine responses. Increased heat‐shock protein expression, hspa1a for Hsp 70, was observed for both TLR2−/− and TLR4−/− mice. This study supports a role for TLR2 and TLR4 in PD and atherosclerosis, corroborating an intricate association between two inflammatory diseases.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".