<i>Enterococcus faecalis</i>lipoteichoic acid suppresses<i>Aggregatibacter actinomycetemcomitans</i>lipopolysaccharide-induced IL-8 expression in human periodontal ligament cells
Bibliographic record
Abstract
Periodontitis is caused by multi-bacterial infection and Aggregatibacter actinomycetemcomitans and Enterococcus faecalis are closely associated with inflammatory periodontal diseases. Although lipopolysaccharide (LPS) of A. actinomycetemcomitans (Aa.LPS) and lipoteichoic acid of E. faecalis (Ef.LTA) are considered to be major virulence factors evoking inflammatory responses, their combinatorial effect on the induction of chemokines has not been investigated. In this study, we investigated the interaction between Aa.LPS and Ef.LTA on IL-8 expression in human periodontal ligament (PDL) cells. Aa.LPS, but not Ef.LTA, substantially induced IL-8 expression at the protein and mRNA levels. Interestingly, Ef.LTA suppressed Aa.LPS-induced IL-8 expression without affecting the binding of Aa.LPS to Toll-like receptor (TLR) 4. Ef.LTA reduced Aa.LPS-induced phosphorylation of mitogen-activated protein kinases, including ERK, JNK and p38 kinase. Furthermore, Ef.LTA inhibited the Aa.LPS-induced transcriptional activities of the activating protein 1, CCAAT/enhancer-binding protein and nuclear factor-kappa B transcription factors, all of which are known to regulate IL-8 gene expression. Ef.LTA augmented the expression of IL-1 receptor-associated kinase-M (IRAK-M), a negative regulator of TLR intracellular signaling pathways, in the presence of Aa.LPS at both the mRNA and protein levels. Small interfering RNA silencing IRAK-M reversed the attenuation of Aa.LPS-induced IL-8 expression by Ef.LTA. Collectively, these results suggest that Ef.LTA down-regulates Aa.LPS-induced IL-8 expression in human PDL cells through up-regulation of the negative regulator IRAK-M.
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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) | 0.001 | 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".