Interleukin (IL)-6 Inhibits IL-27- and IL-30-Mediated Inflammatory Responses in Human Monocytes
Bibliographic record
Abstract
Interleukin (IL)-30, the IL-27p28 subunit of the heterodimeric cytokine IL-27, acts as an antagonist of IL-27 and IL-6 signaling in murine cells via glycoprotein 130 (gp130) receptor and additional binding partners. Thus far, functions of IL-30 have not been fully elucidated in human cells. We demonstrate that like IL-27, IL-30 upregulated TLR4 expression to enhance lipopolysaccharide-induced TNF-α production in human monocytes; however, these IL-30-mediated activities did not reach the same levels of cytokine induction compared to IL-27. Interestingly, IL-30- and IL-27-mediated interferon-γ-induced protein 10 (IP-10) production required WSX-1 engagement and signal transducer and activator of transcription (STAT) 3 phosphorylation; furthermore, IL-30 induced STAT phosphorylation after 16 h, whereas IL-27 induced STAT phosphorylation within 30 min. This prompted us to examine if a secondary mediator was required for IL-30-induced pro-inflammatory functions, and hence we examined IL-6-related molecules. Combined with inhibition of soluble IL-6 receptor α (sIL-6Rα) and data showing that IL-6 inhibited IL-30/IL-27-induced IP-10 expression, we demonstrate a role for sIL-6Rα and gp130 in IL-30-mediated activity in human cells.
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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".