Does IgE-mediated mast cell activation reduce oral tolerance to food antigens?
Bibliographic record
Abstract
Mast cell activation in response to food allergens can result in symptoms such as anaphylaxis. It is unclear how this activation impacts on the immune response to other foods that are present. The objective of this study was to investigate the role of immunoglobulin E (IgE)-mediated mast cell activation in the regulation of oral tolerance to a food antigen using a mouse model. Oral tolerance to common food allergens – egg protein (OVA) or peanut - was established in C57Bl/6 mice. Mice were then immunized and challenged with the relevant food antigen. One group had mast cells activated via IgE/antigen at the site of immunization to food antigen. Antibody responses to food antigen were compared between groups. This was repeated with OVA in mast cell-deficient (kitw-sh/w-sh) mice. Oral tolerance was successfully induced by OVA-feeding as assessed by specific IgE, IgA, IgG1, and IgG2a antibody responses. Tolerance was not maintained in the IgG1 and IgG2a subclasses when mast cells were activated at the site of immunization in OVA-fed animals. OVA-feeding also induced tolerance in mast cell-deficient animals, but IgE/antigen treatment did not modulate OVA-specific antibody production. Feeding peanut butter reduced the anti-peanut IgE response to peanut immunization, but this tolerance was not maintained if mast cells were activated at the site of immunization in peanut-fed animals. These findings suggest that mast cell activation may reduce the effective tolerance to food antigens. This research highlights a possible role for mast cell activation in the development of multiple food allergies that could aid in the design of novel preventative strategies. This work was supported by AllerGen NCE Inc. and by the Natural Sciences and Engineering Research Council.
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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 | 0.001 | 0.001 |
| 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".