Human and mouse mast cells express surface PrP that is shed upon activation and affects degranulation
Bibliographic record
Abstract
Abstract Misfolded prion protein (PrP) is the causative infectious agent in prion diseases in both humans and animals. Despite this, the physiological role of PrP in its native conformation (PrPC) is still poorly understood. While best known for its presence on neurons, PrPC is also present on various immune cells. We determined whether mast cells also expressed surface PrPC and whether PrPC expression was linked to changes in mast cell function. Mast cells are tissue-resident immune cells that modulate many physiologic responses such as allergic inflammation, tissue remodeling, response to infection and neuroplasticity. When activated, mast cells degranulate rapidly, releasing several pre-formed signaling molecules and enzymes. We hypothesized that mast cells expressed PrP and that its expression was influenced by mast cell activation. Human (HMC-1 and LAD2) and rodent (BMMC) mast cell lines expressed surface PrPC with the highest expression in BMMC. Activation of mouse bone marrow derived mast cells (BMMC) with A23187 caused a 12 fold decrease in surface PrP expression after 30 min. Surprisingly, levels of PrP recovered to almost half of untreated levels 3 hrs post-stimulation, and reached similar levels to untreated by 48 hrs. BMMCs derived from PrP knockout mice activated by IgE/antigen released 15–25% less granule contents compared to wild type BMMC, suggesting that PrP is somehow involved in degranulation. There were no significant differences in cell surface expression of the IgE receptor FcɛRI in wild type and knockout BMMC. Our results indicate that PrP is highly expressed on human and rodent mast cells and that it may play an important role in degranulation and activation of mast cells, independent of FcɛRI expression.
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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) | 0.002 | 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".