IL-27 enhances LPS-induced IL-1β in human monocytes and murine macrophages
Bibliographic record
Abstract
IL-27 bridges innate and adaptive immunity by modulating cytokine production from myeloid cells and regulating Th cell differentiation. During bacterial infection, TLR4 triggering by LPS induces IL-27 production by monocytes and macrophages. We have previously shown that IL-27 can prime monocytes for LPS responsiveness by enhancing TLR4 expression and intracellular signaling. If unregulated, this could result in damaging inflammation, whereas on the other hand, this may also provide greater responses by inflammatory processes induced in response to bacterial pathogens. A key process in fine-tuning inflammatory responses is activation of the inflammasome, which ultimately results in IL-1β production. Herein, we investigated the molecular mechanisms by which IL-27 modulates LPS-induced IL-1β secretion in monocytes and macrophages. We found that when delivered simultaneously with LPS, IL-27 augments activation of caspase-1 and subsequent release of IL-1β. Furthermore, we determined that IL-27 primes cells for enhanced IL-1β production by up-regulating surface expression of TLR4 and P2X purinoceptor 7 (P2X7) for enhanced LPS and ATP signaling, respectively. These findings provide new evidence that IL-27 plays an important role in the proinflammatory capacity of monocytes and macrophages via enhancing IL-1β secretion levels triggered by dual LPS-ATP stimulation.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".