Role of IL-12 in macrophage activation during intracellular infection: IL-12 and mycobacteria synergistically release TNF-α and nitric oxide from macrophages via IFN-γ induction
Bibliographic record
Abstract
Abstract IL-12 is believed to play an important role in cell-mediated immunity against intracellular infection primarily by acting on T and NK cells. Recent evidence has suggested, however, that IL-12 has broader cellular targets than previously thought. In this study, we examined the role of IL-12 in macrophage TNF-α and nitric oxide (NO) release by using anin vitro model of intracellular infection. IL-12 alone released relatively little TNF-α and NO, whereas live mycobacteria alone released TNF-α markedly but little NO from murine alveolar macrophages. However, IL-12 and mycobacteria together enhanced TNF-α and NO release synergistically. Because IL-12 and mycobacteria also released IFN-γ from macrophages synergistically, and exogenous IFN-γ with mycobacteria enhanced TNF-α and NO release synergistically, we examined the role of endogenous IFN-γ in IL-12/mycobacteria-stimulated macrophage activation. Using macrophages from mice deficient in IFN-γ, we found that IL-12/mycobacteria-enhanced macrophage TNF-α and NO release was mediated through endogenous IFN-γ. We further demonstrated that IFN-γ and mycobacteria together had a selective effect on macrophage cytokine release because they released TNF-α synergistically but not macrophage chemotactic protein-1 (MCP-1). These findings reveal that IL-12 can activate macrophages potently during intracellular infection, and this activating effect is mediated primarily through its effect on macrophage IFN-γ release.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| 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".