IFN-γ Priming of Macrophages Represses a Part of the Inflammatory Program and Attenuates Neutrophil Recruitment
Bibliographic record
Abstract
Macrophages form a heterogeneous population of immune cells, which is critical for both the initiation and resolution of inflammation. They can be skewed to a proinflammatory subtype by the Th1 cytokine IFN-γ and further activated with TLR triggers, such as LPS. In this work, we investigated the effects of IFN-γ priming on LPS-induced gene expression in primary mouse macrophages. Surprisingly, we found that IFN-γ priming represses a subset of LPS-induced genes, particularly genes involved in cellular movement and leukocyte recruitment. We found STAT1-binding motifs enriched in the promoters of these repressed genes. Furthermore, in the absence of STAT1, affected genes are derepressed. We also observed epigenetic remodeling by IFN-γ priming on enhancer or promoter sites of repressed genes, which resulted in less NF-κB p65 recruitment to these sites without effects on global NF-κB activation. Finally, the epigenetic and transcriptional changes induced by IFN-γ priming reduce neutrophil recruitment in vitro and in vivo. Our data show that IFN-γ priming changes the inflammatory repertoire of macrophages, leading to a change in neutrophil recruitment to inflammatory sites.
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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 | 0.000 |
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".