The Human Antimicrobial Peptide LL-37 Is a Multifunctional Modulator of Innate Immune Responses
Bibliographic record
Abstract
The role of LL-37, a human cationic antimicrobial peptide, in the immune system is not yet clearly understood. It is a widely expressed peptide that can be up-regulated during an immune response. In this report, we demonstrate that LL-37 is a potent antisepsis agent with the ability to inhibit macrophage stimulation by bacterial components such as LPS, lipoteichoic acid, and noncapped lipoarabinomannan. We also demonstrate that LL-37 protects mice against lethal endotoxemia. In addition to preventing macrophage activation by bacterial components, we hypothesized the LL-37 may also have direct effects on macrophage function. We therefore used gene expression profiling to identify macrophage functions that might be modulated by LL-37. These studies revealed that LL-37 directly up-regulates 29 genes and down-regulated another 20 genes. Among the genes predicted to be up-regulated by LL-37 were those encoding chemokines and chemokine receptors. Consistent with this, LL-37 up-regulated the expression of chemokines in macrophages and the mouse lung (monocyte chemoattractant protein 1), human A549 epithelial cells (IL-8), and whole human blood (monocyte chemoattractant protein 1 and IL-8), without stimulating the proinflammatory cytokine, TNFalpha. LL-37 also up-regulated the chemokine receptors CXCR-4, CCR2, and IL-8RB. These findings indicate that LL-37 may contribute to the immune response by limiting the damage caused by bacterial products and by recruiting immune cells to the site of infection so that they can clear the infection.
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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 | 0.000 |
| 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".