Enhancement of Mast Cell IL-6 Production by Combined Toll-Like and Nucleotide-Binding Oligomerization Domain-Like Receptor Activation
Bibliographic record
Abstract
BACKGROUND: Mast cells respond to bacterial infection by producing mediators that recruit and activate leukocytes, mediate vasodilation and induce bronchoconstriction. These mast cell-driven responses play a crucial role in protective immunity against bacterial infection, but may contribute to bacterial exacerbation of allergic diseases. Bacterial components including peptidoglycan (PGN) and lipopeptides are known to activate receptors such as Toll-like receptors (TLR) and nucleotide-binding oligomerization domain-like receptors (NLR). Since the consequences of mast cell activation by individual or combinations of bacterial components have not been fully characterized, we determined the effects of TLR2 and NLR activation, alone or in combination, on human mast cell mediator production. METHODS: Cord blood-derived human mast cells were activated by bacterial PGN, the lipopeptide Pam(3)CSK(4) and NLR agonists alone or in combination. Mast cell degranulation, LTC(4) production and the production of cytokines were assessed. RESULTS: PGN and the lipopeptide Pam(3)CSK(4) induced human mast cells to produce the pro-inflammatory mediators IL-1β, IL-6, CXCL8 and LTC(4) in addition to anti-inflammatory IL-10. NLR agonists alone did not induce these responses, but significantly and selectively increased Pam(3)CSK(4)-mediated mast cell IL-6 production. PGN- and Pam(3)CSK(4)-induced mast cell IL-6, but not IL-1β, production was dependent on adenylyl cyclase activity and could be partially inhibited by the cyclooxygenase inhibitor naproxen. CONCLUSIONS: Increased mast cell IL-6 production in response to combined TLR2 and NLR activation could play a role in the protection against bacterial infection, but potentially exacerbate inflammation-dependent conditions. In addition, mast cell IL-6 production is dependent on adenylyl cyclase activity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".