Host Defense Peptide LL-37 Selectively Reduces Proinflammatory Macrophage Responses
Bibliographic record
Abstract
The human cathelicidin peptide, LL-37, is a host defense peptide with a wide range of immunomodulatory activities and modest direct antimicrobial properties. LL-37 can exert both pro- and anti-inflammatory effects and can modulate the proinflammatory responses of human peripheral blood monocytes and epithelial cells. In this study, we evaluated the effect of LL-37 on mouse bone marrow-derived macrophages (BMDM) and tissue macrophages in vitro and in vivo. LL-37 dramatically reduced TNF-α and NO levels produced by LPS and IFN-γ-polarized M1-BMDM and slightly reduced reactive oxygen species production by these cells. LL-37 did not affect the ability of IL-4-polarized M2-BMDM to upregulate arginase activity, although it did inhibit LPS-induced TNF-α secretion in these cells. LL-37 did not compromise the ability of M1-polarized BMDM to phagocytose and kill bacteria and did not affect the uptake of apoptotic neutrophils by M2-polarized BMDM. However, LL-37-treated M1-BMDM were more efficient at suppressing tumor growth in vitro. LL-37 significantly reduced LPS-induced TNF-α secretion in ex vivo alveolar macrophages, whereas its effect on peritoneal macrophages was much less dramatic. Effective inhibition of LPS-induced TNF-α secretion by alveolar macrophages also occurred in vivo when LL-37 was administered by intratracheal injection. This demonstrates a selective ability of LL-37 to decrease M1-BMDM, M2-BMDM, and tissue macrophage production of the proinflammatory cytokine TNF-α in response to LPS while leaving other crucial anti-inflammatory M1 and M2 macrophage functions unaltered.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".