Bone marrow granulocytes downregulate IL-1β and TNF production and the microbicidal activity of inflammatory macrophages
Bibliographic record
Abstract
Macrophages play critical roles in inflammation and defense against pathogens, as well as in the return to tissue homeostasis. Macrophage subpopulations displaying antagonistic phenotypes are generally classified as proinflammatory M1, implicated in antipathogen and antitumoral activities, or as anti-inflammatory M2, associated with tissue repair. Granulocytic and monocytic myeloid-derived suppressor cells recruited from the bone marrow to tissues and phagocytosis of apoptotic neutrophils can attenuate macrophage microbicidal activity. Here, we showed that bone marrow neutrophils, but not thioglycollate-recruited neutrophils, directly suppress the responses of macrophages that were previously committed to an inflammatory phenotype. Cocultures of inflammatory macrophages with bone marrow CD11b+Ly6Ghi granulocytes led to reduced release of IL-1β, TNF-α, and IL-6 by macrophages after lipopolysaccharide stimulation. The suppressive activity was unrelated to granulocyte apoptosis or to secreted factors and required cell-to-cell contact. The suppressive effect was paralleled by reduction in the nuclear levels of the NF-κB p65 subunit, but not of the p50 subunit. Furthermore, bone marrow granulocytes decreased the phagocytic activity of macrophages and their capacity to kill intracellular Escherichia coli. Taken together, these results show that bone marrow granulocytes can function as suppressors of the proinflammatory activity and microbial-killing responses of macrophages.
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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 | 0.000 |
| 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".