Regulation of macrophage-associated inflammatory responses by species-specific lactoferricin peptides
Bibliographic record
Abstract
Background: Inflammation is the body’s response to injury or infection and is important for healing and eliminating pathogens; however, prolonged inflammation is damaging and may lead to the development of chronic inflammatory disorders. Recently, there has been interest in exploiting antimicrobial peptides (AMPs) that exhibit immunoregulatory activities to treat inflammatory diseases. Methods: In this study, we investigated the immunomodulatory effects of lactoferrin-derived lactoferricin AMPs from three different species (bovine, mouse, and human) with subtle differences in their amino acid sequences that alter their antimicrobial action; to our knowledge, no other studies have compared their immunomodulatory effects. Macrophages, key players in the induction and propagation of inflammation, were used to investigate the effects of species-specific lactoferricin peptides on inflammatory processes. Results: Bovine lactoferricin was the only one of the three peptides studied that downregulated lipopolysaccharide (LPS)-induced pro-inflammatory cytokines, tumor necrosis factor (TNF)-α and interleukin (IL)-6, in both human and mouse macrophages. Lactoferricin regulated inflammation through targeting LPS-activated nuclear factor (NF)-κB and mitogen-activated protein kinase (MAPK) signaling pathways. Although the immunoregulatory role of lactoferricin during an inflammatory response in vivo is yet to be elucidated, further investigation with the use of animal models is warranted by the current findings. Conclusions: The ability of lactoferricin, especially that of bovine origin, to downregulate macrophage-mediated inflammatory responses suggests potential for the development of this peptide as a novel immunotherapeutic agent in the treatment of chronic inflammatory conditions.
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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".