Bovine β-Casein Peptide YPFPGPIH Regulates Inflammation and Macrophage Activity via TLR/NF-κB/MAPK Signaling
Bibliographic record
Abstract
Food-derived bioactive peptides are known to possess immunomodulatory properties, although their molecular mechanisms remain incompletely characterized. In this study, we investigated the immunoregulatory effects and underlying mechanisms of YPFPGPIH, a peptide derived from bovine β-casein, using the RAW264.7 macrophage model. Our results demonstrate that YPFPGPIH enhanced macrophage proliferation and phagocytosis in a dose-dependent manner and promoted chemotactic migration through the upregulation of monocyte chemoattractant proteins MCP-1 and MCP-3. Under lipopolysaccharide (LPS)-induced inflammatory conditions, YPFPGPIH significantly reduced the levels of pro-inflammatory mediators, including interleukin-1β (IL-1β), tumor necrosis factor-α (TNF-α), and nitric oxide (NO), while increasing the production of the anti-inflammatory cytokine interleukin-10 (IL-10), thereby reestablishing cytokine balance. Mechanistic studies revealed that YPFPGPIH inhibited LPS-induced activation of the NF-κB and MAPK pathways, as indicated by reduced nuclear translocation of p65 and decreased phosphorylation of ERK, JNK, and p38. Molecular docking analysis indicated strong binding affinities between YPFPGPIH and Toll-like receptors TLR2 and TLR4, suggesting the involvement of TLR-mediated signaling. Notably, YPFPGPIH downregulated inducible nitric oxide synthase (iNOS) expression and upregulated chemokine mRNA levels, reflecting its dual role in modulating inflammatory and migratory responses. These findings highlight YPFPGPIH as a multifunctional immunomodulatory peptide that fine-tunes macrophage activity through crosstalk between TLR, NF-κB, and MAPK signaling pathways. This study provides new insights for developing peptide-based therapeutics and functional foods aimed at managing inflammatory diseases.
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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".