A type I interferon autocrine–paracrine loop is involved in Toll-like receptor-induced interleukin-12p70 secretion by dendritic cells
Bibliographic record
Abstract
Dendritic cells (DC) produce interleukin-12 (IL-12) in response to Toll-like receptor (TLR) activation. Two major TLR signaling pathways participate in the response to pathogens: the nuclear factor-kappaB (NF-kappaB)-dependent pathway leading to inflammatory cytokine secretion including IL-12 and the interferon (IFN)-dependent pathway inducing type I IFN and IFN-regulated genes. Here we show that the two pathways cooperate and are likely both necessary for inducing an optimal response to pathogens. R-848/Resiquimod (TLR7 ligand in the mouse and TLR7/8 ligand in human) synergized with poly(I:C) (TLR3 ligand) or lipopolysaccharide (LPS; TLR4 ligand) in inducing high levels of bioactive IL-12p70 secretion and IFN-beta mRNA accumulation by mouse bone marrow-derived DC (BM-DC). Strikingly, IL-12p70 but not IL-12p40 secretion was strongly reduced in BM-DC from STAT1(-/-) and IFNAR(-/-) mice. STAT1 tyrosine-phosphorylation, IL-12p35, and IFN-beta mRNA accumulation were strongly inhibited in IFNAR(-/-) BM-DC activated with the TLR ligand combinations. Similar observation were obtained in human TLR8-expressing monocyte-derived DC (moDC) using neutralizing anti-IFNAR2 antibodies, although results also pointed to a possible involvement of IFN-lambda1 (also known as IL-29). This suggests that TLR engagement on DC induces endogenous IFNs that further synergize with the NF-kappaB pathway for optimal IL-12p70 secretion. Moreover, analysis of interferon regulatory factors (IRF) regulation in moDC suggests a role for IRF7/8 in mediating IRF3-independent type I IFN and possibly IL-12p35 synthesis in response to TLR7/8.
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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) | 0.001 | 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) | 0.002 | 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".