HHQ and PQS, two <i>Pseudomonas aeruginosa</i> quorum‐sensing molecules, down‐regulate the innate immune responses through the nuclear factor‐κB pathway
Bibliographic record
Abstract
To explore whether bacterial secreted 4-hydroxy-2-alkylquinolines (HAQs) can regulate host innate immune responses, we used the extracts of bacterial culture supernatants from a wild-type (PA14) and two mutants of Pseudomonas aeruginosa that have defects in making HAQs. Surprisingly, the extract of supernatants from the P. aeruginosa pqsA mutant that does not make HAQs showed strong stimulating activity for the production of innate cytokines such as tumour necrosis factor-alpha and interleukin-6 in the J774A.1 mouse monocyte/macrophage cell line, whereas the extract from the wild-type did not. The addition of 4-hydroxy-2-heptylquinoline (HHQ) or 2-heptyl-3,4-dihydroxyquinoline (PQS, Pseudomonas quinolone signal) to mammalian cell culture media abolished this stimulating activity of the extracts of supernatants from the pqsA mutant on the expression of innate cytokines in J774A.1 cells and in the primary bronchoalveolar lavage cells from C57BL/6 mice, suggesting that HHQ and PQS can suppress the host innate immune responses. The pqsA mutant showed reduced dissemination in the lung tissue compared with the wild-type strain in a mouse in vivo intranasal infection model, suggesting that HHQ and PQS may play a role in the pathogenicity of P. aeruginosa. HHQ and PQS reduced the nuclear factor-kappaB (NF-kappaB) binding to its binding sites and the expression of NF-kappaB target genes, and PQS delayed inhibitor of kappaB degradation, indicating that the effect of HHQ and PQS was mediated through the NF-kappaB pathway. Our results suggest that HHQ and PQS produced by P. aeruginosa actively suppress host innate immune responses.
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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".