Manipulation of innate immunity by a bacterial secreted peptide: Lantibiotic nisin Z is selectively immunomodulatory
Bibliographic record
Abstract
Innate immunity is triggered by a variety of bacterial molecules, resulting in both protective and potentially harmful pro-inflammatory responses. Further, innate immunity also provides a mechanism for the maintenance of homeostasis between the host immune system and symbiotic or non-pathogenic microorganisms. However, the bacterial factors that mediate these protective effects have been incompletely defined. Here, it was demonstrated that the lantiobiotic nisin Z is able to modulate host immune responses and mediate protective host immunity. Nisin Z induced the secretion of the chemokines MCP-1, IL-8 and Gro-α, and significantly reduced TNF-α induction in response to bacterial LPS in human PBMC. The results correlated with the ability of nisin Z to confer protection against both the Gram-positive organism Staphylococcus aureus, and the Gram-negatives Salmonella enterica sv. Typhimurium and Escherichia coli in murine challenge models. Mechanistic studies revealed that nisin Z modulates host immunity through similar mechanisms as natural host defense peptides, engaging multiple signal transduction pathways and growth factor receptors. The results presented herein demonstrate that, in addition to nisin Z, other bacterial cationic peptides and, in particular, the lantibiotics, could represent a new class of secreted bacterial molecule with immunomodulatory activities.
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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) | 0.000 | 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".