TLR9 signaling regulates tissue factor and tissue factor pathway inhibitor expression and activity in human coronary artery endothelial cells
Bibliographic record
Abstract
Although human endothelial cells recognize and respond to bacterial DNA (CpG DNA), the role of bacterial DNA in procoagulation is not known. We investigated the impact of bacterial DNA on expression and activity of tissue factor (TF) and tissue factor pathway inhibitor (TFPI) in human coronary artery endothelial cells (HCAEC). CpG DNA (1–32 μg/ml) markedly induced TF expression between 4 and 8 hours in both protein and mRNA levels and TF activity. CpG DNA markedly enhanced NF‐êB activation, which was blocked by the telomere‐derived TLR9 antagonist oligonucleotide TTAGGG. Consistently, the specific NF‐êB inhibitors BAY 117082 and SN50 prevented CpG DNA‐induced TF transcription and secretion. Conversely, culture of HCAEC with CpG DNA for 24 to 48 hours reduced TFPI transcription, secretion, and activity. Methylation of cytosines in CpG DNA resulted in a complete loss of biological activities. Our results demonstrate that bacterial DNA through TLR9 can alter the balance of TF and TFPI expression and activity in HCAEC, thereby contributing to thrombus formation, the major cause of acute coronary artery disease.(Grant support: CIHR MOP‐97742).
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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 | 0.000 |
| 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 | 0.001 |
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".