Self-Assembled Peptide Nanoparticle-Mediated Macrophage Polarization Enhances Anticancer Efficacy of Chemotherapeutics in Triple-Negative Breast Cancer
Bibliographic record
Abstract
Peptide-based self-assembled materials are being investigated as a biodegradable option with various biomedical applications such as drug-delivery carriers and as biosensors. In this study, we report the mechanism of action of a synthetic β-sheet and turn rich macrocyclic host defense peptide that self-assemble in the form of nanoparticles and stimulate naïve and tumor-associated macrophages, resulting in the production of pro-inflammatory mediators in macrophage monoculture and in a macrophage/triple-negative breast cancer (TNBC) coculture model. Our results show that macrocyclic peptide-based nanomaterials termed as mCA4 engage with toll-like receptors of macrophages, activating downstream pathways in both naïve and IL-4-pretreated macrophages, and result in the production of pro-inflammatory mediators by the immune cells. The immunomodulatory potential of mCA4 is highly sequence-specific, and while an inactive analogue prepared by swapping a single amino acid in the peptide chain does not diminish the self-assembly properties of the parent peptide, its impact on the immunomodulatory potential is detrimental. The immunomodulatory potential of mCA4 is further confirmed in macrophage monoculture and in macrophage/TNBC coculture by proteomics and by Western blot analysis. The treatment of macrophage/TNBC coculture with mCA4 and chemotherapeutics enhanced anticancer efficacies of chemotherapeutics, suggesting immuno-adjuvant-like properties of materials for TNBC treatment.
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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".