Mast Cell Proteases Cleave Prion Proteins and a Recombinant Ig against PrP Can Activate Human Mast Cells
Bibliographic record
Abstract
IgE Abs, best known for their role in allergic reactions, have only rarely been used in immunotherapies. Nevertheless, they offer a potential alternative to the more commonly used IgGs. The affinity of IgE Ag binding influences the type of response from mast cells, so any immunotherapies using IgEs must balance Ag affinity with desired therapeutic effect. One potential way to harness differential binding affinities of IgE is in protein aggregation diseases, where low-affinity binding of endogenous proteins is preferred, but enhanced binding of clusters of disease-associated aggregated proteins could target responses to the sites of disease. For this reason, we sought to create a low-affinity IgE against the prion protein (PrP), which exists in an endogenous monomeric state but can misfold into aggregated states during the development of prion disease. First, we determined that mast cell proteases tryptase and cathepsin G were capable of degrading PrP. Then we engineered a recombinant IgE Ab directed against PrP from the V region of a PrP-specific IgG and tested its activation of the human mast cell line LAD2. The αPrP IgE bound LAD2 through Fc receptors. Crosslinking receptor-bound αPrP IgE activated SYK and ERK phosphorylation, caused Fc receptor internalization, and resulted in degranulation. This work shows that a recombinant αPrP IgE can activate LAD2 cells to release enzymes that can degrade PrP, suggesting that IgE may be useful in targeting diseases that involve protein aggregation.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".