Immunomodulatory Activities of Small Host Defense Peptides
Bibliographic record
Abstract
Recent studies have demonstrated that in addition to their antimicrobial activity, cationic host defense peptides, like the human cathelicidin LL-37, perform many activities relating to innate immunity, including the induction or modulation of chemokine and cytokine production, alteration of gene expression in host cells, and inhibition of proinflammatory responses of host cells to bacterial components such as lipopolysaccharide (LPS) in vitro and in vivo. To investigate if these properties are shared by smaller peptides, two cathelicidin peptides derived from bovine neutrophils, the 13-mer indolicidin and Bac2A, a linear 12-amino-acid derivative of bactenecin, were compared to the 37-amino-acid peptide LL-37. Indolicidin, like LL-37, inhibited LPS-induced tumor necrosis factor alpha (TNF-alpha) secretion, even when added up to an hour after the addition of Escherichia coli O111:B4 LPS to the human macrophage/monocyte-like THP-1 cell line. In contrast, Bac2A demonstrated no significant antiendotoxin activity. At low concentrations, indolicidin and LL-37 acted synergistically to suppress LPS-induced production of TNF-alpha. Indolicidin was analogous to LL-37 in its ability to induce the production of the chemokine interleukin-8 (IL-8) in a human bronchial cell line, 16HBE14o(-), but it was unable to induce production of IL-8 in THP-1 cells. In contrast, Bac2A was unable to induce IL-8 in either cell type. Conversely, Bac2A was chemotactic for THP-1 cells at concentrations between 10 and 100 mug/ml, while indolicidin and LL-37 were not chemotactic at these concentrations for THP-1 cells. This indicates that in addition to the potential for direct microbicidal activity, cationic host defense peptides may have diverse and complementary abilities to modulate the innate immune response.
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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".