Antibiotic-induced <i>Malassezia</i> spp. expansion in infants promotes early-life immune dysregulation and airway inflammation in mice
Bibliographic record
Abstract
Abstract Antibiotics have deleterious consequences for the gut microbiome and can increase the risk of childhood asthma. While the effects of antibiotics on the bacterial microbiome and asthma risk are well characterized, their impact on the fungal microbiome (mycobiome) remains vastly unexplored. We investigated the effect of antibiotic use on the gut mycobiome in an observational, prospective clinical study of young infants. Antibiotic treatment resulted in increased fungal abundance and expansion of the yeast Malassezia spp. Based on these findings, germ-free mouse pups were colonized with a defined consortium of mouse-derived bacteria (Oligo-MM12) with or without Malassezia restricta . Colonization with this yeast increased myeloid and lymphoid intestinal immune responses deemed critical in atopy development, and elevated airway inflammation in house-dust mite (HDM)-challenged mice. Further evaluation in eosinophil-deficient mice revealed that the observed immune response is partially dependent on this cell type. This translational work demonstrates that fungal overgrowth and expansion of Malassezia spp. are previously overlooked collateral effects of infant antibiotic use, which may offer a potential strategy to prevent or mitigate pediatric asthma and related conditions. One Sentence Summary: Antibiotic-induced Malassezia spp. expansion in infants promotes early-life immune dysregulation and airway inflammation in gnotobiotic mice.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".