Polymer-coated nanoparticle protein corona formation potentiates phagocytosis of bacteria by innate immune cells and inhibits coagulation in human plasma
Bibliographic record
Abstract
Nanoparticles (NPs) that are exposed to blood are coated with an assortment of proteins that establish their biological identity by forming the interface between the NP and the cells and tissues of the body. The biological relevance of this protein corona is often overlooked during toxicological assessments of NPs. However, accurate interpretation of biological outcomes following exposure to NPs, including activation of coagulation, opsonization of pathogens, and cellular phagocytosis, must take this adsorbed proteome into account. In this study, we examined protein coronas on the surface of five poly(acrylic acid) (PAA) metal-oxide NPs (TiO2, CeO2, Fe2O3, ZnO, and PAA-capsules) following exposure to human plasma for key markers of various host response pathways, including humoral immunity and coagulation. We also evaluated the impacts of pre-exposing serum proteins to PAA-NPs on the opsonization and phagocytosis of bacteria by two immune cell lines. Results demonstrated that each PAA-NP type adsorbed a unique profile of blood proteins and that protein-coated PAA-NPs significantly inhibited human plasma coagulation with PAA-zinc oxide NPs and their associated proteome fully abrogating clotting. Protein-coated PAA-NPs also resulted in a 50% increase in phagocytic activity of RBL-2H3 cells and a 12.5% increase in phagocytic activity in the RAW 264.7 cell line. We also identified numerous structural, coagulation, and immune-activating proteins in the adsorbed protein corona, which resulted in altered biological function. Overall, our findings demonstrate that the formation of protein coronas on the surface of NPs plays an important role in directing the biological outcomes of opsonization, cell phagocytosis, and blood coagulation.
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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".