Microbial metabolism of food allergens determines the severity of IgE-mediated anaphylaxis
Bibliographic record
Abstract
SUMMARY Anaphylaxis is an acute, potentially life-threatening reaction, often triggered by foods and largely mediated by IgE. Critically important to anaphylaxis are the factors that modulate its severity. The human microbiota is known to influence oral tolerance, but the microbial mechanisms directly involved in IgE-mediated anaphylaxis remain unknown. Here, we demonstrate that human saliva and jejunum harbor peanut-degrading bacteria that metabolize immunodominant allergens (Ara h 1 and 2) and alter IgE-binding. Additionally, we provide in vivo evidence that oral bacteria metabolize peanut allergens, influencing systemic allergen exposure and anaphylaxis severity. Finally, in clinical studies, we observe that common peanut-degrading bacteria, such as Rothia, from the oral cavity, are more abundant in peanut-allergic patients who exhibit better tolerance to allergen exposure. Altogether, these results demonstrate that human microbiota modulates IgE-mediated reactions through allergen metabolism. We reveal a novel microbial mechanism with potential to prevent, or reduce, the severity of IgE-mediated anaphylaxis.
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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) | 0.003 | 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".