Perinatal antibiotic exposure alters composition of murine gut microbiota and may influence later responses to peanut antigen
Bibliographic record
Abstract
BACKGROUND: Accumulating evidence suggests that the gut microbiota shapes developmental processes within the immune system. Early life antibiotic use is one factor which may contribute to immune dysfunction and the recent surge in allergies by virtue of its effects on gut microbiota. OBJECTIVE AND METHODS: As a first step towards determining whether a relationship exists between perinatal antibiotic induced changes in the gut microbiota and the later development of a peanut allergy, we exposed newborn mice to either the broad-spectrum antibiotic vancomycin or to a vehicle for 6 weeks and then used a novel murine model of peanut allergy. RESULTS: Early-life treatment with vancomycin resulted in a significant shift in the gut microbiota community characterized by a reduction in the abundance of firmicutes and preponderance of inflammatory proteobacteria. Mice with an antibiotic-altered microbiota, showed a localized allergic-like response characterized by ear swelling and scratching following intra-dermal peanut antigen challenge. Likewise, circulating IgE levels were increased in antibiotic-treated mice, but no evidence of a systemic allergic or anaphylactic-like response was observed. Importantly, we utilized the naturally occurring pro-inflammatory cytokine, tumor necrosis factor-α (TNF-α), rather than the more commonly used cholera toxin, as an adjuvant together with the peanut antigen. CONCLUSION: Our data suggest that early antibiotic exposure promotes a shift in the gut microbiota community that may in turn, influence how mice later respond to a TNF-α + antigen challenge. However, further studies verifying the capacity of microbiota restoration to protect against allergic responses will be needed to confirm a causal role of antibiotic-induced microbiota variations in promoting allergic disease phenotypes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".