Bacterial DNA Activates Endothelial Cells and Promotes Neutrophil Adherence through TLR9 Signaling
Bibliographic record
Abstract
TLR9 detects bacterial DNA (CpG DNA) and elicits both innate and adoptive immunity. Recent evidence indicates that TLR9 is expressed in more diverse cell types than initially thought. In this study, we report that HUVECs constitutively express TLR9 and selectively recognize unmethylated CpG motifs in bacterial DNA and synthetic immune stimulatory CpG oligodeoxynucleotides. HUVECs respond to CpG DNA with rapid phosphorylation of IkappaB-alpha and NF-kappaB-mediated gene transcription and surface expression of the adhesion molecules ICAM-1 and E-selectin independent of MAPK signaling. The telomere-derived TLR9 inhibitory oligonucleotide 5'-TTT AGG GTT AGG GTT AGG G-3', agents that block endosomal acidification such as chloroquine and bafilomycin A, and NF-kappaB inhibitors abrogated CpG DNA-induced signaling. HUVEC activation by CpG DNA led to markedly enhanced neutrophil adhesion under nonstatic conditions that was further enhanced when neutrophils were stimulated with CpG DNA. The adhesive interactions were blocked by Abs against CD18 and, to a lesser degree, by anti-E-selectin and anti-L-selectin Abs. Our findings demonstrate that bacterial DNA promotes beta(2) integrin and E-selectin-mediated HUVEC-neutrophil adherence, and indicate the ability of CpG DNA to initiate and/or maintain the inflammatory response.
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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".